This paper discusses advancements in chronically implantable electrocorticography (ECoG) interfaces, highlighting a novel modular system utilizing an encapsulated neural interfacing acquisition chip (ENIAC) that offers improved spatiotemporal resolution and wide area coverage for brain monitoring. ECoG presents a minimally invasive alternative to EEG and penetrating electrodes, addressing limitations in resolution and long-term reliability. The authors explore challenges and solutions related to the design of next-generation ECoG systems, including recording, miniaturization, and wireless communication.

1. INVITED
P A P E R
Silicon-Integrated High-Density
Electrocortical Interfaces
This paper examines the state of the art of chronically
implantable
electrocorticography (ECoG) interface systems and introduces a
novel modular
ECoG system using an encapsulated neural interfacing
acquisition chip (ENIAC)
that allows for improved, broad coverage in an area of high
spatiotemporal
resolution.
By Sohmyung Ha, Member IEEE, Abraham Akinin, Student
Member IEEE,
Jiwoong Park, Student Member IEEE, Chul Kim, Student
Member IEEE,
Hui Wang, Student Member IEEE, Christoph Maier, Member
IEEE,
Patrick P. Mercier, Member IEEE, and Gert Cauwenberghs,
Fellow IEEE
ABSTRACT | Recent demand and initiatives in brain research

2. have driven significant interest toward developing chronically
implantable neural interface systems with high spatiotempo-
ral resolution and spatial coverage extending to the whole
brain. Electroencephalography-based systems are noninva-
sive and cost efficient in monitoring neural activity across the
brain, but suffer from fundamental limitations in spatiotem-
poral resolution. On the other hand, neural spike and local
field potential (LFP) monitoring with penetrating electrodes
offer higher resolution, but are highly invasive and inade-
quate for long-term use in humans due to unreliability in
long-term data recording and risk for infection and inflamma-
tion. Alternatively, electrocorticography (ECoG) promises a
minimally invasive, chronically implantable neural interface
with resolution and spatial coverage capabilities that, with
future technology scaling, may meet the needs of recently
proposed brain initiatives. In this paper, we discuss the chal-
lenges and state-of-the-art technologies that are enabling
next-generation fully implantable high-density ECoG inter-

3. faces, including details on electrodes, data acquisition front-
ends, stimulation drivers, and circuits and antennas for
wireless communications and power delivery. Along with
state-of-the-art implantable ECoG interface systems, we
introduce a modular ECoG system concept based on a fully
encapsulated neural interfacing acquisition chip (ENIAC).
Multiple ENIACs can be placed across the cortical surface,
enabling dense coverage over wide area with high spatio-
temporal resolution. The circuit and system level details of
ENIAC are presented, along with measurement results.
KEYWORDS | BRAIN Initiative; electrocorticography; neural
recording; neural stimulation; neural technology
I. INTRODUCTION
The Brain Research through Advancing Innovative Neuro-
technologies (BRAIN) Initiative envisions expanding our
understanding of the human brain. It targets development
and application of innovative neural technologies to ad-
vance the resolution of neural recording, and stimulation
toward dynamic mapping of the brain circuits and process-

4. ing [1], [2]. These advanced neurotechnologies will enable
new studies and experiments to augment our current under-
standing of the brain, thereby enabling tremendous advances
in diagnosis and treatment opportunities over a broad range
of neurological diseases and disorders.
Studying the dynamics and connectivity of the brain
requires a wide range of technologies to address multiple
temporal and spatial scales. Fig. 1 shows spatial and tem-
poral resolutions and spatial coverage of the various brain
monitoring methods that are currently available [3]–[6].
Noninvasive methods such as magnetic resonance im-
aging (MRI), functional magnetic resonance imaging
Manuscript received January 1, 2016; revised May 24, 2016;
accepted May 30, 2016.
Date of publication August 5, 2016; date of current version
December 20, 2016. This
work was supported by the University of California
Multicampus Research Programs
and Initiatives (MRPI) and the University of California San
Diego Center for Brain
Activity Mapping.
The authors are with the University of California San Diego, La
Jolla, CA 92093-0412
USA (e-mail: [email protected]; [email protected];
[email protected]).

5. Digital Object Identifier: 10.1109/JPROC.2016.2587690
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See
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(fMRI), magnetoencephalography (MEG), and positron
emission tomography (PET) provide whole-brain spatial
coverage. Although fMRI achieves high spatial resolution
down to 1 mm, its temporal resolution is severely limited
(1–10 s) as the system measures neural activity indirectly
by quantifying blood oxygenation to support regions with
more elevated metabolism. In contrast, MEG provides
higher temporal resolution (0.01–0.1 s) at the expense of
poor spatial resolution (1 cm). Whereas fMRI and MEG
provide complementary performance in spatiotemporal
resolution, PET offers molecular selectivity in functional

6. imaging at the expense of lower spatial (1 cm) and tempo-
ral (10–100 s) resolution, and the need for injecting posi-
tron emitting radionuclides in the bloodstream. However,
neither fMRI and MEG, nor PET are suitable for wearable
or portable applications, as they all require very large, ex-
pensive, and high power equipment to support the sensors
as well as extensively shielded environments.
In constrast, electrophysiology methods, which di-
rectly measure electrical signals that arise from the activity
of neurons, offer superior temporal resolution. They have
been extensively used to monitor brain activity due to their
ability to capture wide ranges of brain activities from the
subcellular level to the whole brain oscillation level as
shown in Fig. 2(a). Due to recent advances in electrode
and integrated circuit technologies, electrophysiological
monitoring methods can be designed to be portable, with
fully wearable or implantable configurations for brain–
computer interfaces having been demonstrated.
One of the most popular electrophysiological moni-

7. toring methods is electroencephalography (EEG), which
records electrical activity on the scalp resulting from
volume conduction of coherent collective neural activity
throughout the brain, as illustrated in Fig. 2(a). EEG re-
cording is safe (noninvasive) and relatively inexpensive,
but its spatiotemporal resolution is limited to about 1 cm
and 100 Hz, due largely to the dispersive electrical prop-
erties of several layers of high-resistive tissue, particu-
larly skull, between the brain and the scalp. In contrast,
recording with intracranial brain-penetrating microelec-
trodes [labeled as EAP + LFP in Fig. 2(a)] can achieve
much higher resolution due to the much closer proximity
to individual neurons. Thus, it is also widely used
for brain research and brain–computer interface (BCI)
applications. Using microelectrodes, extracellular action
potential (EAPs) and local field potentials (LFPs) can be
recorded from multiple neurons across multiple cortical
areas and layers. Even though penetrating microelec-
trodes can provide rich information from neurons, they

8. can suffer from tissue damage during insertion [7]–[9],
Fig. 1. Spatial and temporal resolution as well as spatial
coverage
of various neural activity monitoring modalities [3]–[5]. For
each
modality shown, the lower boundary of the box specifies the
spatial resolution indicated on the left axis, whereas the upper
boundary specifies the spatial coverage on the right axis. The
width of each box indicates the typical achievable range of
temporal resolution. Portable modalities are shown in color.
Bridging an important gap between noninvasive and highly
invasive techniques, �ECoG has emerged as a useful tool for
diagnostics and brain-mapping research.
Fig. 2. (a) Conventional electrophysiology methods including
EEG,
ECoG, and neural spike and LFP recording with penetrating
microelectrodes. Both EEG and ECoG can capture correlated
collective volume conductions in gyri such as regions of a-b, d-
e,
and j-k. However, they cannot record opposing volume
conductions in sulci such as regions of b-c-d and e-f-g and

9. random dipole layers such as regions of g-h and l-m-n-o [18].
(b) Emerging fully implantable �ECoG technologies enabled by
flexible substrate ECoG microarrays and modular ECoG
interface
microsystems. Such technologies are capable of capturing local
volume conducting activities missed by conventional methods,
and are extendable to cover large surface area across cortex.
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and have substantial limitations in long-term chronic
applications due to their susceptibility to signal degrada-
tion from electrode displacement and immune response
against the electrodes [10]. Because of the more extreme
invasiveness and longevity issues, chronic implantation
of penetrating microelectrodes in humans is not yet
viable.

10. Between the two extremes of EEG and penetrating
microelectrode arrays, a practical alternative technique is
electrocoticography (ECoG), or intracranial/intraoperative
EEG (iEEG), which records synchronized postsynaptic po-
tentials at locations much closer to the cortical surface, as
illustrated in Fig. 2(a). Compared to EEG, ECoG has high-
er spatial resolution [11]–[13], higher signal-to-noise ratio,
broader bandwidth [14], and much less susceptibility to ar-
tifacts from movement, electromyogram (EMG), or elec-
trooculargram (EOG) [15], [16]. In addition, ECoG does
not penetrate the cortex, does not scar, and can have supe-
rior long-term signal stability recording through subdural
surface electrodes.
ECoG recording was pioneered in the 1920s by
Hans Berger [17]. He recorded ECoG signals with elec-
trodes placed on the dural surface of human patients.
In the 1930s through 1950s, Wilder Penfield and
Herbert Jasper at the Montreal Neurological Institute
used ECoG to identify epileptogenic zones as a part of the

11. Montreal procedure, which is a surgical protocol to treat
patients with severe epilepsy by removing sections of the
cortex most responsible for epileptic seizures. In addi-
tion, intraoperative electrical stimulation of the brain has
been used to explore the functional mapping of the brain
including brain areas for speech, motor, and sensory
functions. This localization of important brain regions is
important to exclude from surgical removal. Although
ECoG is still the gold standard for decoding epileptic
seizure foci and determining target regions for surgical
removal, the role of ECoG has been reduced due to re-
cent advances in imaging techniques for functional brain
mapping such as fMRI, PET, and MEG.
With advances in high channel count and wireless op-
eration, however, ECoG has again emerged as an impor-
tant tool not only for more effective treatment of
epilepsy, but also for investigating other types of brain
activity across the cortical surface. ECoG recording pro-
vides stable brain activity recording at a mesoscopic spa-

12. tiotemporal resolution with a large spatial coverage up to
whole or a significant area of the brain. Advanced minia-
turized electrode arrays have pushed the spatial resolu-
tion of ECoG recording to less than 1 mm, offering a
unique opportunity to monitor large-scale brain activity
much more precisely. Moreover, wireless implantable
microsystems based on flexible technology or via modu-
lar placement of multichannel active devices, both illus-
trated in Fig. 2(b), have recently emerged as a new
paradigm to record more closely to the cortical surface
(in many cases on top of the pia), while enabling
coverage along the natural curvature of the cortex with-
out penetration. These micro ECoG, or �ECoG, devices
enable even higher spatial resolution than conven-
tional ECoG systems, and are beginning to enable next-
generation brain mapping, therapeutic stimulation, and
BCI systems.
This paper discusses the challenges of designing
next-generation ECoG interfaces, including recording,

13. miniaturization, stimulation, powering, and data com-
munications.
Solution
s are presented by first surveying
state-of-the-art technologies, and then through a de-
tailed exploration of a state-of-the-art modular system
implementation.
II. ECoG INTERFACES: RECORDING
AND STIMULATION
A. Volume Conduction With Differential Electrodes
Volume conduction of ionic currents in the body is
the source of biopotentials such as EEG, ECoG, ECG,
and EMG. In the frequency band of interest for biopo-
tential recording (typically less than 1 kHz), the quasi-

14. static electric field equations with conductivities of tissue
layers are a good approximate representation [19]. To
first order, a volume conducting current monopole I
spreads radially through tissue with an outward current
density of magnitude I=4�r2 at distance r, giving rise to
an outward electric field of magnitude I=4��r2 and a
corresponding electrical potential I=4��r, where � is the
tissue volume conductivity.
1) Differential Recording: For EEG, a current dipole as
a closely spaced pair of opposing current monopoles is
typically an adequate model representing distant sources
of synchronous electrical activity across large assemblies
of neurons or synapses [19]. In contrast, for implanted
neural recording including ECoG and single-unit neural
spike/LFP recording, a set of monopole currents result-
ing from individual neural units is a more appropriate

15. model at the local spatial scale, especially for high
density recording with electrodes spaced at dimensions
approaching intercellular distances. Since the volume
conducting currents from neural action potentials are
spatially and temporally distributed, only a few effective
current sources at a time are typically active near an
electrode, one of which is illustrated in the vicinity of
two closely spaced electrodes in Fig. 3(a). Furthermore,
unlike the ground-referenced recording with single-
ended electrodes for EEG, high-density electrode arrays
typically require differential recording across electrodes,
particularly in �ECoG integrated recording since the
miniaturized geometry does not allow for a distal ground
connection.

16. In Fig. 3(a), the recorded differential voltage V as a
function of the distances rþ and r� of the two electrodes
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from a current source I induced by the activity of adja-
cent neurons can be expressed as
Vðrþ; r�Þ ffi
I
2��
1

17. rþ
� 1
r�
� �
(1)
valid for distances rþ; r� substantially larger than the
electrode diameter D. Although the expression is similar
to that for an EEG current dipole recorded with a single
electrode, it is fundamentally different in that here the
difference in monopole activity results from differential
sensing with two closely spaced electrodes rather than
from dipolar distribution of two closely spaced currents.
The factor 2 rather than 4 in the denominator arises
from the semi-infinite boundary conditions along the
horizontal plane of the electrode substrate, in that
volume conduction is restricted to the tissue below the

18. substrate. Fig. 3(b) shows a spatial map of the effect of a
current source located in tissue below the electrode pair,
with electrode diameter D and pitch 2D, on the mea-
sured differential voltage V. Its recording penetration
depth is roughly 2D, the electrode pitch, vertically, and
about 4D horizontally. Note again that this is for a single
monopolar source; in the presence of dipolar activity
with two opposing nearby currents (i.e., charge balancing
across a soma and dendrite of a neuron extending below
the electrodes) the measured voltage (1) becomes double
differential, leading to a quadrupolar response profile.
2) Differential Stimulation: The same pair of closely
spaced electrodes can be used for differential stimulation
by injecting currents into the surrounding tissue. Again,
the absence of a distal ground connection in miniature
integrated electrode arrays necessitates local charge bal-

19. ancing so that the currents through the two electrodes
need to be of equal strength and opposing polarity, con-
stituting a current dipole sourced within the electrode
array. The resulting differential current stimulation can
be modeled with the diagram shown in Fig. 3(c). The
current dipole from the pair of differential stimulation
currents flowing through the two electrodes induces an
electrical field ~E in the brain tissue expressed by
~Eðrþ; r�Þ ffi
I
2��
~urþ
r2þ
�~ur�
r2�

20. !
(2)
where again rþ; r� � D, and ~urþ and ~ur� represent unit
vectors pointing outwards along the direction of rþ and
r�, respectively. With the same electrode configuration
of Fig. 3(b), the magnitude of the electric field j~Ej is
shown in Fig. 3(d) indicating a shallow region near the
electrodes being electrically stimulated. Note that the
electrical field for stimulation is inversely proportional to
the square of distance to each electrode, while the poten-
tial measured for recording is inversely proportional to
linear distance. Thus, the available depth of differential
stimulation is shallower than that of differential record-
ing. In general, the penetration depth of stimulation and
recording are roughly a few times larger than the elec-

21. trode pitch.
3) Electrode Array Configurations: While this is a
simplified model, it is sufficiently representative to dem-
onstrate the effectiveness of differential electrode config-
urations for both recording and stimulation without a
global reference, as required for fully integrated �ECoG
in absence of a distal ground connection. As the analysis
and simulations above show, the spatial response of
differential recording and stimulation are quite localized
near the electrode sites, on a spatial scale that matches
the electrode dimensions and spacing. Thus, aside
from spatial selection of recording or stimulation along
the 2-D surface by translation of selected pairs of adja-
cent electrodes, depth and spatial resolution of recording
or stimulation can be controlled via virtual electrode

22. pitch, by pooling multiple electrodes in complementary
pairs of super electrodes at variable spacing between
centers.
Fig. 3. (a) Neural recording setting with closely spaced
differential electrodes interfacing below with neural tissue of
volume conductivity �. (b) Spatial map of the effect of the
location of a current source þI in the tissue on recorded
differential voltage V, in units 1=�D. (c) Neural stimulation
setting
with differential currents injected into the surrounding tissue
through the same two closely spaced electrodes. (d) Spatial map
of the resulting electric field magnitude j~Ej in the tissue,
in units 1=�D2.
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B. Electrode Interfaces for ECoG
Electrodes, which couple ECoG signals from the brain
into the analog front-end (AFE) amplifiers, are the first
interface to ECoG systems. Thus, their properties,
including materials, geometries, and placement are of
crucial importance in building entire acquisition and
actuation systems [32].
Given the distance between the scalp and individual
neuronal current sources and sinks, EEG recording is un-

24. suitable for detecting small local field potentials as
shown in Fig. 2(a). Electrical dipole signals travel a mini-
mum distance of 1 cm between the outer surface of the
cerebral cortex to the scalp, including layers of cerebro-
spinal fluid, meninges, bone and skin, all with varying
electrical properties. Through this path the effect of a
small-localized dipole source is not only greatly attenu-
ated but also spatially averaged among a myriad of neigh-
bors, resulting in practical and theoretical limits to the
spatiotemporal resolution of EEG [33]. As implied in (1),
centimeter-sized electrode arrays with centimeter spac-
ings in conventional ECoG recordings are better than
EEG, but have limitations in resolving current sources of
neural activity of size smaller than the electrode pitch. A

25. conventional clinical ECoG array with electrodes at the
centimeter scale is depicted in Fig. 4(a).
A miniaturized surface electrode array in direct con-
tact with the cerebral cortex can resolve the activity of
smaller source populations down to millimeter or even
submillimeter resolution as shown in Fig. 4(b)–(k). Fur-
thermore, site-specific purposeful electrical stimulation
is only possible at �ECoG scale. Improvements in the
quality and applications of ECoG data have resulted from
technological developments at the interface: microfabri-
cation of electrode and substrate materials and intercon-
nect. Simply miniaturizing existing electrode array is not
typically sufficient: for example, miniaturized electrodes
Fig. 4. Conventional and state-of-the-art ECoG electrode arrays.

26. (a) Example of a conventional electrode array placed on the
subdural
cortex (top) with post-operative radiograph showing electrode
array placement (bottom). The pitch and diameter of electrodes
are
1 cm and 2 mm, respectively [20]. (b) �ECoG electrode array
placed along with a conventional ECoG electrode array [21],
[22].
(c) Patient-specific electrode array for sulcal and gyral
placement [23]. (d) Flexible 252-channel ECoG electrode array
on a thin
polyimide foil substrate [24]. (e) �ECoG electrode array with
124 circular electrodes with three different diameters [25].
(f) Parylene-coated metal tracks and electrodes within a silicone
rubber substrate [26]. (g) A transparent �ECoG electrode array
with
platinum electrodes on a Parylene C substrate [27]. (h) An
electrode array with poly (3, 4-ethylenedioxythiophene) (PDOT)
and

27. PEDOT-carbon nanotube (CNT) composite coatings for lower
electrode interface impedance [28]. (i) A flexible electrode
array on a
bioresorbable substrates of silk fibroin [29]. (j) Flexible active
electrode array with two integrated transistors on each pixel for
ECoG
signal buffering and column multiplexing for high channel
count [30]. (k) Flexible ECoG array with embedded light-
emitting diodes
for optogenetics-based stimulation [31].
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28. in G 1 mm pitch arrays have very high impedance, which
results in poor signal quality, and reduced charge trans-
fer capacity, which typically reduces stimulation effi-
ciency. Such limitations have been addressed by
micropatterning increased surface area, and carbon nano-
tube [34] or conductive polymer coatings [Fig. 4(h)]
[28]. Flexible substrates have also reduced the effective
distance between source and electrodes through tight,
conformal geometries [24], [26]. Aside from creating
ultraflexible thin materials, dissolvable substrates leave
behind a mesh of thin unobtrusive wires and electrodes
with a superior curved conformation and biocompatibil-
ity as shown in Fig. 4(i) [29], [35].
Even with advances in flexible electrode arrays, the

29. number of channels in practical systems is still limited to
approximately 100 because of the high density of intercon-
nections between electrode arrays and corresponding
acquisition systems. Active electrodes are an emerging
approach to maximize number of electrode channels while
maintaining a small number of wired connections to the
electrode array. Advanced fabrication techniques can pro-
duce arrays of electrodes with direct integration of transis-
tors on the flexible substrate as shown in Fig. 4(j) [30] This
approach can be supplemented with additional in situ
devices capable of multiplexing several hundreds of record-
ing channels, thereby reducing the required number of
wires and interconnections. Another emergent approach

30. combining active recording electrodes and new polymeric
materials has led to the development of organic electro-
chemical transistors in ECoG arrays [36]. However, one
limitation of current active arrays is that the same elec-
trode cannot be used for stimulation. Recently, transparent
electrode arrays with integrated light path for simulta-
neous ECoG recording and optogenetic stimulation have
been demonstrated as shown in Fig. 4(k) [31]. The active
development of novel electrode interfaces has not only im-
proved conventional ECoG recording, but also generated
new applications and therapeutic opportunities.
C. Integrated Circuit Interfaces for
Data Acquisition
Neural data acquisition with a high spatial resolution

31. poses several challenges in the design of application-
specific integrated circuits (ASICs) used to perform data
acquisition. Higher channel density in ECoG arrays typi-
cally results in smaller electrode size, and if the area
overhead of the ASIC should be kept small, as is desired
in most applications, then the area dedicated to amplify
and digitize each channel should also reduce. Unfortu-
nately, area trades off with several important parameters.
For example, a more dense array of AFE amplifiers dissi-
pates more power and generates more heat for each
channel, and thus power dedicated to each front-end
channel must reduce to meet thermal regulatory limits.
Power then trades off with noise, causing signal fidelity

32. issues. The area/volume constraint of front-ends typically
also precludes the use of external components such as
inductors or capacitors. As a result, alternating current
(ac) coupling capacitors employed to reject direct cur-
rent (dc) or slowly time-varying electrode offsets are not
typically employed, so other techniques are instead nec-
essary. Small electrodes also have higher impedance, re-
quiring even higher AFE input impedance to avoid signal
attenuation. In addition, high power supply rejection ra-
tio (PSRR) is required because miniaturized implants
typically condition dc power from an external ac source,
and further may not be able to accommodate large power
decoupling capacitors. Higher channel counts also re-

33. quire higher communication throughput, increasing the
power consumption of communication. All these require-
ments are interrelated and trade off with each other in
many ways, as indicated in Table 1 [32], [37], [38].
The noise-current tradeoff in instrumentation ampli-
fiers (IAs) is well represented by the noise efficiency
factor (NEF), which is expressed as
NEF ¼ Vrms;in
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffiffi
2Itot
�Vt � 4kT � BW
r
(3)
where Vrms;in is the total input-referred noise, Itot the

34. total current drain, Vt the thermal voltage, and BW the
3-dB bandwidth of the system [39]. To minimize noise
with a given current consumption or minimize current
consumption with a upper-bound noise limit, various
Table 1 Design Factors and Tradeoffs in Integrated ECoG
Interfaces
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design techniques have been proposed and demonstrated
to address this challenges [40]–[59]. Such techniques to
minimize NEF include 1) utilizing the weak inversion re-

35. gion of CMOS operation to maximize transconductance
efficiency [37], [40], [60], [61]; 2) chopper stabilization
techniques to reduce 1=f noise and other low-frequency
noise [41], [43], [45], [47], [48], [62], [63]; 3) dynamic
range manipulation to reduce power supply voltages
[54], [55] using spectrum-equalizing AFE [64], [65]; and
4) using current-reusing nMOS and pMOS input pairs to
maximize transconductance and achieve an NEF below
two [54], [56]–[59] (Fig. 5).
Challenges in meeting the other specifications listed
in Table 1 have also been addressed using various cir-
cuit techniques. For example, several dc-coupling IAs
have been demonstrated in order to avoid external ac-

36. coupling capacitors at the input of the AFE [42], [43].
In these designs, electrode offsets are canceled by feed-
back currents via a dc-servo loop [42], [43] or by capac-
itive feedback [66].
Integration of higher channel count on a single
chip has been pursued, as well. Thus far, chips with
approximately 100–300 data acquisition channels have
been reported [54], [67]–[70]. One of the strategies
to reduce area and power consumption in order to
maximize channel density is the use of scaled pro-
cesses such as 65-nm complementary metal–oxide–
semiconductor (CMOS) [66], achieving 64 channels
with a silicon area of 0.025 mm
2
per channel. For high-

37. er density, in some designs, a SAR ADC is shared by
about 8–16 AFEs using a time multiplexer [54]. In doing
so, power-efficient multiplexers [71] and time-interleaving
sample-and-hold circuits in SAR ADCs have been demon-
strated. Alternatively, a dedicated ADC per AFE channel
has been also pursued due to its ease of integration with a
larger number of channels [44], [66].
D. Integrated Circuit Interfaces for Stimulation
Historically, electrical stimulation on the cortical sur-
face was pioneered by Penfield [72] as intraoperative
planning for epileptic patients, demonstrating the local-
ized function of different regions of the cortex [73].
Since then, functional neural stimulation has been exten-
sively investigated and developed during the past de-

38. cades, making great progress for various clinical
applications such as deep brain stimulation, cochlear im-
plants, cardiac pacemakers, bladder control implants,
and retinal prostheses. Given that many epilepsy patients
already require implantation of ECoG monitoring instru-
mentation, there is a great opportunity for closed-loop
electrical control of seizure activity at much higher reso-
lution and precision than transcranial electric [74] and
transcranial magnetic stimulation [75], [76]. These em-
bedded stimulators would not require any additional in-
vasive risks, and could potentially prevent more drastic
treatments such as partial removal of the cortex. An
implantable recording and stimulation system can con-

39. tain a digital signal processor capable of deciding when
to stimulate [77]. Other applications of cortical stimula-
tion include closed-loop brain computer interfaces (BCI)
which aim to generate functional maps of the brain [78],
restore somatosensory feedback [76], restore motor con-
trol to tetraplegics [79], aid stroke survivors [80], [81],
restore vision [82], reduce pain [73], or even change
emotional state [83].
Pushing the form factor and channel density of the
neural interface systems to the limit requires addressing
several challenges in ASICs for stimulation. Smaller form
factor and higher channel density require smaller elec-
trode size, which limits charge transfer capacity for

40. effective stimulation. Hence, higher voltage rails of more
than �10 V and/or high-voltage processes are required
typically [84]–[86], in turn this results in higher power
consumption, larger silicon area, and system complexity
to generate and handle high-voltage signals. Instead of
maintaining a constant high-voltage power supply, some
designs save power by generating a large power rail only
when actively stimulating [77], [87], [88].
For further power savings, adiabatic stimulation has
been also actively investigated. Adiabatic stimulators gen-
erate ramping power rails that closely follow the voltages
at the stimulation electrode, minimizing unnecessary
voltage drops across the current source employed for
conventional constant-current stimulation. Various

41. designs have been implemented with external capacitors
[89], external inductors [90], and charge pumps [87].
Still, there is much room for improvement in the imple-
mentation of adiabatic stimulators in fully integrated,
miniaturized implantable ICs.
It is generally desired to minimize the area occupied
per stimulation channel for high-density integration.
To date, integration of 100–1600 channels has been
Fig. 5. Noise efficiency factors of state-of-the-art
instrumentation amplifiers for biopotential recording
applications.
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achieved [84]–[86], [91]–[93]. In order to integrate such
high channel counts, programmability of waveform pa-
rameters, individual connectivity to each channel, and/or
charge balancing need to be compromised to some ex-
tent. For example, groups of 4–8 electrodes in [91],
[93]–[95] can share a single digital-to-analog converter
for optimized, high density integration.
Safety is of the utmost importance in chronic neural
interfaces, so charge balancing is imperative [96]. Resid-
ual dc results in tissue damage, production of toxic by-

43. products, and electrode degradation [96]. However, it is
quite challenging to assure charge balance for each chan-
nel in high-channel neural interface systems. One of the
most straightforward strategies is to employ serial dc-
blocking capacitors, inherently forcing the net dc to be
zero all the time. This method has been employed for
neural stimulation applications [97]–[99] due to its in-
trinsic safety when area permits. However, the required
blocking capacitance is often prohibitively large for on-
chip integration, and is thus inadequate for high-density
and/or miniaturized implants. Instead of external capaci-
tors, capacitive electrodes made with high-k dielectric
coatings have been investigated for safe neural interfaces

44. [88], [100], [101]. Several other techniques for better
charge balancing have been demonstrated: 1) shorting
electrodes to ground [102]; and 2) utilizing a discharging
resistor [94], active current balancing by feedback con-
trol [103], [104], generating additional balancing current
pulses by monitoring electrode voltages [105], and em-
bedded DAC calibration [93], [106].
E. Integrated Electrocortical Online Data
Processing
The integration of signal processing with neurophysi-
ological sensing and actuation enables real-time online
control strategies toward realizing adaptive, autonomous
closed-loop systems for remediation of neurological dis-
orders [107], [108]. Online signal processing of ECoG

45. data has tremendous potential to improve patient out-
comes in diseases currently lacking therapy or requiring
resection of otherwise healthy neural tissue such as in-
tractable epilepsy. As one of the treatments for epilepsy,
functional neurostimulation in response to detected sei-
zures has been proved effective in reduction of seizures
[109], [110]. For real-time closed-loop therapeutics, on-
line automated seizure prediction and/or detection based
on ECoG or EEG recordings of epileptic patients is im-
perative [111]–[114], and their on-chip implementation
has been actively investigated and demonstrated utilizing
extraction and classification of various signal features
such as power spectral densities and wavelet coefficients

46. [47], [77], [115]–[118].
In addition, ECoG has proven a powerful modality for
BCI applications owing to richer features present in the
higher resolution ECoG signals compared to surface EEG,
which can be harnessed to more precisely infer sensory
recognition, cognition, and motor function. Since ECoG-
based BCI systems widely utilize spectral power density
for their inputs [119], frequency band power extraction
techniques have been implemented immediately follow-
ing AFEs avoiding digitization and RF data transmission
of whole ECoG raw signals [120], [121].
Such on-chip real-time ECoG data processing offers
two distinct advantages over offline as well as online off-

47. chip processing. First, constrains on data bandwidth and
power consumption on the implant can be largely re-
lieved. In many implementations, raw recorded data are
wirelessly streamed out and delivered to either a unit
worn on the top of the head, or directly to a local base
station such as a smartphone. The power of such ap-
proaches is typically proportional to the communication
distance. Thus, the overall power consumption of designs
that stream over long distances can be dominated by the
power of communication circuits [47]. In order to reduce
system-level power consumption, several on-chip data
processing techniques have been applied for EEG- and
ECoG-based BCI systems and epileptic seizure detection.
By doing so, power consumption of RF data transmission

48. can be drastically reduced [47]. Second, local processing
may alleviate stringent latency and buffer memory
requirements in the uplink transmission of data for
external processing, especially where multiple implants
are time-multiplexed between a common base station.
III. SYSTEM CONSIDERATIONS
A. Powering
Major challenges in implantable medical devices
(IMDs) for high-density brain activity monitoring are
fundamentally posed by their target location. Some of
these IMDs can be wholly placed on the cortex within a
very limited geometry as shown in Fig. 2(b) In other
cases, only the electrode array is placed on the cortex

49. while the other components can be located in the empty
space created by a craniotomy [122], or under the scalp
with lead wires connected [123], [124]. Regardless of
placement, this constrained environment poses a difficult
power challenge.
There are three primary methods for powering an
implanted device: employing a battery, harvesting en-
ergy from the environment, and delivering power
transcutaneously via a wireless power transmitter [125],
[126]. A natural first choice would be a battery, as
they have been extensively used in other implantable
applications such as pacemakers. While it makes sense
to use a battery in a pacing application, where the
power of the load circuit is small (microwatts) and a

50. large physical volume is available such that the battery
can last ten years or more, the power consumption in
high-density neural recording and stimulation applica-
tions is typically much larger (milliwatts), and the
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physical volume available for a large battery is small,
combining to dramatically reducing operational lifetime
prior to necessary surgical reimplantation. The medical

51. risks of regular brain surgery and recovery, just to re-
place a battery, are unacceptable to most patients, and
thus batteries are typically only employed in high-density
neural applications as temporary energy storage in sys-
tems with a different power source: either energy scav-
enging or wireless power transfer.
Harvesting energy from ambient sources in the local
environment has been a potentially attracting powering
option since at least the 1970s during the development
of cochlear implants. Many scavenging methods continue
to be actively developed: 1) solar cells; 2) biofuel cells;
3) thermoelectric generators; 4) piezoelectric generators;
5) ambient RF, etc. While such approaches are theoreti-

52. cally attractive, the limited volume available near the
brain, coupled with the stochastic nature of many energy
harvesting sources, results in power that is too small and
too variable to reliably operate multichannel neural
technologies.
The most popular means to power an implanted
device with higher power than single-channel pacing
applications is to wirelessly delivery power via a transcu-
taneous link. Power can be delivered transcutaneously
using one of three primary mechanisms: 1) optics (typi-
cally near infrared light); 2) acoustics (typically at ultra-
sound frequencies); and 3) electromagnetics (either
near-, mid-, or far-field waves). Each method can deliver
from 10 �W up to the megawatt range of power. How-

53. ever, the total deliverable power highly depends on the
geometry and makeup of the receiving transducer, along
with the implant depth and orientation.
Optical powering through transmission of infrared
light has a very short penetration depth of a few millime-
ters, limiting its utility to subcutaneous and very shallow
implant applications [130]–[132]. Ultrasound, on the
other hand, can penetrate much deeper into tissue, po-
tentially powering implants located on the cortical sur-
face. In fact, it has been demonstrated that ultrasound
can more efficiently power millimeter-scale devices im-
planted deep into soft tissues than electromagnetic ap-
proaches [133]. However, it has also been shown that
ultrasonic energy does not efficiently penetrate bone,

54. limiting opportunities to directly power cortical implants
from outside the skull. To overcome this, researchers
have proposed two-tiered systems, where electromag-
netic energy is coupled through the skull, then converted
to acoustic energy via an intermediate transducer system,
and finally delivered to the miniaturized implant through
soft tissue [134]. However, in addition to nontrivial pack-
aging and transducer design challenges, this is likely only
a reasonable approach when the implant to be powered
is either very deep, or very small (submillimeter scale).
For these reasons, ultrasonic power delivery is not typi-
cally considered for ECoG systems.
The most popular transcutaneous power delivery ap-

55. proach utilizes electromagnetics. For devices implanted
to a depth of a few centimeters, and that are on the
order of millimeter-to-centimeter in diameter, near- or
mid-field electromagnetic power transfer is generally
considered to be the most efficient and practical method
to power such devices. Near-field power transfer, which
operates at frequencies up to approximately 100 MHz for
typical implants, has been extensively used for cochlear
implants [135], retinal prostheses [86], [93], and various
research IMD systems [122], [136]–[139], and has been
investigated and characterized to maximize its usage and
power transfer efficiency for implants [140]–[145].
Most conventional designs operate in the near-field

56. between 1 and 20 MHz, since it is well known that con-
ductivity (and hence losses) in tissue increase at higher
frequencies, as shown in Fig. 6. Operating at higher fre-
quencies, it was previously argued, would encounter
higher losses and thus be less efficient. In addition, gov-
ernmental regulatory agencies limit the amount of power
that can be dissipated in tissue for safety reasons—the
U.S. Federal Communications Commission (FCC) sets a
specific absorption rate (SAR) of less than 1.6 W/kg, for
example. For these reasons, conventional transcutaneous
power transfer links operate in the low-megahertz range,
often at the 6.78- and 13.56-MHz ISM bands [125],
[144], [145].
However, it is also well known that the quality factor

57. and radiation resistance of electrically small coil anten-
nas increases with increasing frequency. Thus, miniatur-
ized implants, which have electrically small coils for
wireless power reception, tend to prefer to operate at
higher frequencies, at least in air. In biological tissues,
the tradeoff between coil design and tissue losses results
in an optimal frequency for wireless power transmission
where efficiency is maximized. For example, the induc-
tance of coils located on miniaturized, millimeter-scale
implants ranges from 10 to 100 nH [66], [88], [146],
[147]. To compensate for reduced magnetic flux through
the miniaturized receiving coil, the carrier frequency for
wireless power transfer should be increased, often into

58. the hundreds of megahertz to single-digit gigahertz range
[66], [146]–[149]. These prior studies have demonstrated
that it is possible to efficiently deliver milliwatts of
power to small, implanted devices under regulatory
limits, and thus electromagnetic approaches are the pri-
mary means to deliver power to implanted ECoG
devices.
B. Wireless Data Communication
Implanted ECoG monitoring devices need to convey
the acquired data to the external world through wireless
communication. The information received by the exter-
nal base station can be monitored, processed, and used
by users and care takers for health monitoring, treat-

59. ments, or scientific research. For ECoG monitoring
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implants, data transmission from the implant to the ex-
ternal device, known as uplink or backward telemetry,
requires much higher data rate and is subject to more
stringent power consumption constraints than data trans-
mission from the external device to the implant, known
as downlink or forward telemetry, because the available

60. power and geometric volume are much smaller on the
implanted side than the external side. This power con-
straint on backward data transmission is more exacer-
bated as the number of channels increases, as higher
data rates are required in a typically more compact area,
leading to severe power density challenges.
Backward data communications typically employ elec-
tromagnetics operating either in the far- or near-field.
Far-field communication uses electromagnetic radiation
to transmit data over a distance much longer than the
size of the actual device. Hence, the implant can send
data to an external base station located up to a few
meters, such as mobile phone. Far-field up-conversion
transmitters are currently the most well-established com-

61. munication technology. Due to the wide availability of
far-field radio products and a myriad of different infra-
structures (e.g., Bluetooth Low Energy, WiFi, etc.), far-
field radios can be quickly adopted for robust operation
[122]. However, even state-of-the-art low-power radios
consume 9 1 nJ/b [125], [164], which is order-of-
magnitude larger than what typical ECoG recording
IMDs require.
As an alternative far-field transmission method, im-
pulse radio ultrawideband (IR-UWB) transmission has
emerged recently due to its low power consumption in
the range of a few tens of picojoule per bit (pJ/b) [165]–
[167]. Avoiding generation of a carrier with an accurate
frequency, noncoherent IR-UWB transmitters generates

62. short pulses with ON–OFF keying (OOK) or pulse posi-
tion modulation (PPM). Due to its high and wide fre-
quency range (3.1–10 GHz), data rates of more than
10 Mb/s with tens of picojoules per bit have been re-
ported. [168] In addition, antennas for this type of trans-
mission do not need to be large. However, there are a
couple of critical reasons against their usage for IMDs
[125]. Foremost, their peak transmission power is large
due to the inherently duty-cycled nature of IR-UWB
transmitters. Thus, while the average power may be low,
a large high-quality power supply with a large battery of
capacitor is required to supply large peak currents, which
may be prohibitively large for many ECoG applications.

63. Moreover, since IR-UWB operates at very high frequency
over 3 GHz, tissue absorption rate is higher.
In contrast to far-field communication methods, near-
field radios operate over short distances, typically within
one wavelength of the carrier frequency, and are thus
suitable for use when an external device is located di-
rectly on the head. In fact, since this configuration is
naturally present in wirelessly powered devices, near-
field communication can easily be implemented along
with this wireless powering. One of the most popular
data communication methods that can be implemented
along with forward power delivery is the backscattering
method [169]–[173]. This method modulates the load

64. conditions of forward powering signals, and reflecting
this energy back to the interrogator. Since only a single
switch needs to be driven, the power consumed on the
Fig. 6. (a) Relative permittivity and specific conductivity over
frequency range from 10 to 100 GHz with most popular
frequency communication bands for ECoG implants [127],
[128].
(b) Relative permittivity and (c) specific conductivity of various
kinds of tissues including skin, fat, skull, CSF, gray and white
matter [129].
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implant side is minimal, as carrier generation and active
driving of an antenna are not required. Since, with this
technology, a few picojoules per bit to tens of picojoules
per bit can be achieved [173], it has been widely adopted
by various IMDs [66], [86], [174]–[178]. However, exter-
nal data reception in backscattering systems can be
challenging in some cases because of the large power dif-
ference between the large power carrier signal and the
weak backscattered signal.
Forward telemetry for neural recording IMDs is typi-
cally used for sending configuration bits to the implant,
requiring a relatively low data rate typically much less

66. than 100 kb/s. Hence, amplitude shift keying (ASK) has
been widely employed in such IMDs [88], [179], [180],
modulating the power carrier signals. For IMDs with
stimulation capability, forward telemetry for closed-loop
operation is typically time-multiplexed with backward
telemetry.
C. Hermetic Encapsulation
Implanted devices containing silicon ICs need pack-
aging in a protective enclosure to mitigate corrosion and
other contamination by surrounding electrolyte in the
body [181], [182]. Thus far, titanium-, glass- or ceramic-
based enclosures have been the primary means for
hermetic sealing in long-term implants, since these hard
materials have been shown to be biocompatible and

67. impermeable to water [181]. Even though such hard en-
closures are used in the majority of long-term implants
[6], [111], [122], [150]–[155], [159], [183], their very
large volume and weight, typically much larger than the
ICs and supporting components they contain, prohibits
their use in high-dimensional neural interfaces heavily
constrained by anatomical space such as retinal prosthe-
ses [184] and �ECoG arrays [30]. In addition, hard pack-
aging requires intricate methods for hermetic sealing of
feedthroughs to polymer insulated extensions of the im-
plant such as electrode array cabling, limiting the density
of electrode channels due to feedthrough channel spac-
ing requirements.

68. To overcome these challenges, conformal coating of
integrated electronics with polymers such as polyimide,
silicone, and parylene-C have been investigated as
alternatives. They are superior over metal, glass, and
ceramic hard seals in miniaturization, flexibility, and
compatibility with the semiconductor process [185].
However, polymers are susceptible to degradation and
are not long-term impermeable to body fluids. While
polymer encapsulation has been used for relatively
simple and short-term (less than 1–2 years) implants,
substantial improvements are needed for viable solu-
tions to long-term hermetic encapsulation [182], [186].
Recent next-generation advances in miniaturized her-
metic sealing of silicon integrated circuits, such as

69. multilayer multimaterial coating [187], and encapsula-
tion using liquid crystal polymers (LCPs) [185], are
promising developments toward highly miniaturized
implantable electronics for chronic clinical use. Fur-
thermore, recent advances in dissolvable flexible elec-
tronics [188], [189] offer alternatives to hermetic
encapsulation for acute applications without the need
for post-use surgical extraction.
IV. STATE-OF-THE-ART ECoG
INTERFACE SYSTEMS
Various types of implantable devices for ECoG interfaces
have been developed for clinical use and neuroscience
research. Their target applications include treatment of
neurological disorders and ECoG-based BCIs. Fig. 7 illus-

70. trates several state-of-the-art ECoG interface systems for
clinical and research applications.
On the clinical side, implantable devices shown in
Fig. 7(a)–(c) have been developed mostly for use in
closed-loop treatment of intractable epilepsy as an alter-
native to tissue resection. These devices monitor ECoG
signals and deliver stimulation to the seizure foci in re-
sponse to epileptic seizure detection. The NeuroPace
RNS System, shown in Fig. 7(a), is the first such sys-
tem to receive FDA approval for closed-loop treatment
in epilepsy patients, proven effective to reduce the fre-
quency of partial-onset seizures in human clinical trials
[191], [192].

71. However, clinically proven devices are severely lim-
ited in the number of ECoG channels, typically less than
ten, and rely on batteries, limiting implantation life time
up to a few years. In addition, their physical size is too
large to be implanted near the brain, so the main parts
of these systems are implanted under the chest with a
wired connection to the brain. Further developments in
ECoG technology have striven to conquer these chal-
lenges: increasing number of channels, wireless power-
ing, and miniaturization.
One such device is the Wireless Implantable Multi-
channel Acquisition system for Generic Interface with
NEurons (WIMAGINE), which features up to 64 chan-

72. nels, targeting long-term ECoG recording fully implanted
in human patients [122] as shown in Fig. 7(d). This active
IMD (AIMD) is fully covered by a 50-mm-diameter her-
metic housing made of silicone-coated titanium with a
silicone-platinum electrode array on the bottom side. Its
silicone over-molding is extended to include two anten-
nas for RF communication and wireless power transfer.
The housing fits inside of a 50-mm craniotomy, and its
upper surface is just below the skin, with the implant re-
placing the previously existing bone. Two 32-channel
ECoG recording ASICs [193] are implemented for ECoG
recording, and commercial off-the-shelf components are
employed for data processing, communication, power
management, etc., leading to relatively high power

73. consumption—75 mW for 32 channels. Its operation and
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biocompatibility has been evaluated in vivo in nonhuman
primates.
Another example is the BrainCon system with
16-channel ECoG recording and 8-channel stimulation
designed for chronic implanted use in closed-loop hu-
man BCI [158]. Shown in Fig. 7(e), this device consists

74. of an ECoG electrode array and an electronic package
with a magnet, an inductive coil, and electronic compo-
nents for data acquisition, stimulation, and communica-
tion. Targeted for long-term recording and cortical
stimulation in human patients, it is enclosed in a her-
metic package with medical grade silicone rubber [156],
[157], and was validated in vivo for more than ten
months [158].
Further miniaturization and advances in functionality
have been pursued through integration of circuits for
ECoG recording, wireless powering, and wireless com-
munication as shown in Fig. 7(f)–(j). In addition, low-
cost ECoG interfaces shown in Fig. 7(k) for acute animal
research have been developed [163], as have ECoG inter-

75. faces with transparent electrode arrays for compatibility
with optogenetic stimulation shown in Fig. 7(l) [27].
Each of these devices offers substantial advances in
wireless and integrated ECoG technology with improved
functionality and increased density and channel counts.
Yet, most rely on substantial cabling in connecting to the
array of electrodes, or at least a wired connection to a
distal ground as reference.
Fig. 7. State-of-the-art ECoG interfacing systems. (a)
NeuroPace RNS system [150], [151]. (b) NeuroVista seizure
advisory system [111],
[152], [153]. (c) The neural interface (NI) system of Medtronic
[154], [155]. (d) The Wireless Implantable Multi-channel
Acquisition
system for Generic Interface with NEurons (WIMAGINE) [122].
(e) BrainCon system for a general-purpose medical BCI [156]–

76. [158].
(f) The Wireless Human ECoG-based Real-time BMI System
(W-HERBS) [6], [159]. (g) �ECoG recording system of Cortera
Neurotechnologies, Inc. [66], [160]. (h) A ECoG recording
system with bidirectional capacitive data Telemetry [161]. (i)
An ECoG
recording system with a carbon nanotube microelectrode array
and a corresponding IC [34]. (j) A wireless ECoG interface
system with
a 64-channel ECoG recording application-specific integrated
circuit (ASIC) [162]. (k) An 8-channel low-cost wireless neural
signal
acquisition system made with off-the-shelf components [163].
(l) �ECoG recording system with an electrode array fabricated
on a
transparent polymer for optogenetics-based stimulation [27].
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V. FULLY INTEGRATED MODULAR
�ECoG RECORDING AND STIMULATION
A. Encapsulated Neural Interfacing Acquisition
Chip (ENIAC)
As highlighted above, most current state-of-the-art
ECoG ASICs rely on external components, such as flexi-
ble substrates, electrode arrays, and antennas [27], [30],
[66], [122], [156]–[158], [161], [162]. In doing so, inte-
gration of all the components into a complete system
requires special fabrication processes, and, importantly,

78. requires a large number of connections between the
readout ASIC and the electrodes, which are very diffi-
cult to manage in a hermetic environment. Further-
more, electrodes typically make direct metal-electrolyte
contact to the surrounding tissue, which can lead to
generation of toxic byproducts during electrical stimula-
tion. In addition, most systems do not support electrical
stimulation, while those that do offer limited stimula-
tion efficiency, or require large external components
for efficient operation. Finally, the silicon area occupied
by the ASIC limits the span and density of electrodes
across the cortical surface. For ultrahigh channel count
experiments, as needed for next-generation neurosci-

79. ence and called for by several brain initiatives, such
limitations must be overcome.
Instead of separating the electrodes and the ASIC, a
promising approach that we present below is to integrate
everything on a single encapsulated neural interfacing
and acquisition chip (ENIAC), including electrodes, an-
tennas for power and data telemetry, and all other cir-
cuits and components [88]. Thus, no external wires,
substrates, batteries, or any other external components
are required. Complete encapsulation of the ENIAC with
a biocompatible material removes direct contact to tis-
sue, including the electrodes for recording and stimula-
tion. As such, the chip itself serves as a complete

80. standalone neural interfacing system.
As shown in Fig. 8 the ENIAC is designed to be small
enough ð3 � 3 � 0:25 mm3Þ to be placed among the
folds and curves of the cortical surface [see Fig. 2(b)],
and to be implanted through small skull fissures. Hence
it offers greater coverage of the cortical surface while be-
ing much less obtrusive than other minimally invasive
ECoG approaches, permitting even insertion without sur-
gery. As seen in the block diagram in Fig. 8, the chip
contains an LC resonant tank, electrodes, recording
channels, stimulator, power management units, and bidi-
rectional communication circuits.
Its first prototype, fabricated in a 180-�m CMOS
silicon-on-insulator (SOI) process, is shown on the upper

81. right side of Fig. 8. With two turns and 100-�m
Fig. 8. Encapsulated neural interfacing acquisition chip
(ENIAC) [88], [190]. (a) System diagram showing the fully
integrated
functionality of the ENIAC comprising on-chip antenna,
electrodes, and all the circuitry for power management,
communication, ECoG
recording and stimulation. No external components are needed,
and galvanic contact to surrounding tissue is completely
eliminated in
the fully encapsulated device. (b) Chip micrograph and
dimensions of the prototype ENIAC.
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82. thickness, the on-chip coil results in an inductance of
23.7 nH. The same single coil is shared for wireless
power transfer and bidirectional RF communication.
Sixteen electrodes, which can be individually configured
as recording or stimulating channels, are integrated di-
rectly on the top metal layer of the chip. To enhance en-
ergy efficiency and remove the need for separate
rectification and regulation stages, an integrated resonant
regulating rectifier IR3 [190] is implemented. In addition,
an adiabatic stimulator generates constant-current stimu-
lation pulses from the RF power input in an adiabatic
manner, much more energy efficient than conventional
stimulation from dc static power supplies.

83. B. Power and Communication
As highlighted in Section III-A, RF inductive powering
is the most efficient means for power delivery at this im-
plantation depth, and has been adopted in ENIAC. To
model the inductive link through tissue, a detailed finite
element method (FEM) model of the octagonal loop trans-
mitter antenna and the 3 � 3 � mm2 ENIAC shown in
Fig. 9(a) was constructed in ANSYS HFSS, using tissue
spectral permittivity and absorption properties as shown in
Fig. 6(b) and (c). Optimal power transfer between the
transmitter coil and ENIAC is reached at a resonance fre-
quency of 190 MHz as shown in Fig. 9(b). At this fre-
quency, substantially more than the required 2-mW power
can be delivered under the specific absorption rate (SAR)

84. limit (2 W/kg in IEEE std. 1528). Fig. 9(c) shows the maxi-
mum transmit power at the SAR limit, and corresponding
maximum deliverable power at the implant, for varying
distance of the air gap between the loop transmitter and
the scalp. The optimal distance for maximum power deliv-
ery, trading between reduced SAR-limited transmit power
at lower distance and increased path losses at higher dis-
tance [194], was found to be around 5 mm.
ENIAC minimizes power losses in the received power
from the RF coil owing to an integrated resonant regulat-
ing rectifier ðIR3Þ architecture that combines power
management stages of rectification, regulation, and dc
conversion, eliminating typical losses due to inefficien-
cies at each stage when implemented separately. As illus-

85. trated in Fig. 10(a), IR3 generates a constant power
supply 0.8 V independent of fluctuation in the LC tank
voltages. IR3 operates by adapting both width and fre-
quency of pulsed rectifier switching based on a feedback
signal derived from VDD [190].
Concurrently, the amplitude-shift-keying (ASK) de-
modulator tracks and amplifies the envelope of the LC
tank voltages to decode transmitted configuration data as
illustrated on the bottom of Fig. 10(a). The ASK commu-
nication is used to wirelessly configure the operation
modes and parameters of the chip. To synchronize data
reception, a 16-b predetermined identification code is
used as prefix followed by serial peripheral interface
(SPI) signals.

86. Fig. 10(b) shows test setup and sample data for the
IR3 power delivery and the ASK data transmission. For
these tests, a primary coil built on a printed-circuit board
Fig. 9. (a) Three-dimensional finite element method (FEM)
modeling of brain tissue layers between external transmitter and
implanted ENIAC. (b) Simulated forward transmission
coefficient
S21 and maximum available gain (MAG) from the transmitter to
the implanted ENIAC. The optimal frequency for wireless
power
transfer is around 190 MHz. (c) Maximum transmit power
limited
by specific absorption rate (SAR) and maximum receivable
power
at the implanted ENIAC, as a function of distance of air gap

87. between the transmitter and the scalp, optimum around 5 mm.
Green arrows denote minimum path losses at each air distance.
24 Proceedings of the IEEE | Vol. 105, No. 1, January 2017
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was placed 1 cm above the ENIAC. The top right panel
in Fig. 10(b) shows the measured coil voltages simulta-
neously rectified and regulated by the IR3 [190], [195]
to produce the supply voltage VDD � 0:8 V. The total
transmitted power is about 5 mW, of which around
80 �W is received by the ENIAC. The bottom panel

88. of Fig. 10(b) shows the AM modulated input on the
primary side, and the demodulated ASK signal in the
ENIAC.
C. Recording
The recording module integrates 16 AFEs, a 16:1
analog multiplexer (MUX), and an analog-to-digital
converter (ADC) as shown in Fig. 11(a). Each of the
16 capacitively coupled electrodes is connected either to
its local AFE channel, or to the global stimulator, multi-
plexed by a high-voltage tolerant switch matrix. The AFE
amplifies the biopotential VIN1 from the capacitively
coupled noncontact electrode with two amplification
stages and a common-mode averaging circuit. The common-
mode averaging circuit constructs a single reference signal
VAVG as the average of all VINi electrode voltages through

89. capacitive division. Similar to differential recording across
a pair of adjacent electrodes (Section II-A1), the internal
common-mode reference VAVG allows single-ended record-
ing over all 16 electrodes without the need for a distal
external ground connection. A pMOS-based pseudoresis-
tor [in the inset of Fig. 11(a)] is used to set the dc oper-
ating point at VREF for the capacitive division to allow for
very high (T�-range) resistance in very small silicon area
[40], [196].
The first low-noise amplifier stage has a noninverting
configuration with a feedback capacitor C1 and a common-
mode coupling capacitor of 39 � C1, which connects to
the common-mode averaging node VAVG, resulting in a
differential voltage gain of 40 (V/V). VAVG is buffered
and used for common-mode rejection in the second AFE
stage. The second AFE stage provides variable gain by
manipulating the connections of two capacitors, con-

90. nected either as input or as feedback capacitors [46],
[54]. Output signals of the AFEs are multiplexed and
buffered to the SAR ADC, which has time-interleaving
sample-and-hold input DACs to ensure longer sampling
time, leading to power saving in buffering the input
DAC of the ADC.
Measurement results for the AFE, characterizing its
frequency response and noise performance, are shown in
Fig. 11(b) and (c). Variable 50–70-dB gain is supported,
Fig. 11. (a) Circuit diagram of the recording module of ENIAC
with
16 AFE channels, 16:1 analog multiplexer (MUX), and
successive
approximation register (SAR) ADC. (b) Measured frequency
and

91. noise characteristics of one AFE channel.
Fig. 10. (a) System diagram of ENIAC power management and
ASK forward communication, sharing the same single on-chip
loop antenna. The integrated resonant regulating rectifier ðIR3Þ
generates a stable 0.8-V dc output voltage directly from the
190-MHz RF coil voltage while the ASK demodulator decodes
and
amplifies the modulated signal. (b) Simplified test setup for
wireless powering and communication along with measurement
samples at the transmitter and the receiver.
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and the input-referred noise is 1.5 �Vrms at 2.45 �A sup-
ply current for a noise efficiency factor (NEF) of 4.
D. Stimulation
As illustrated in Fig. 12(a), ENIAC on-chip electrodes
are implemented on top metal, as used for bond pads
and on-chip inductors. The exposed electrodes allow for
direct coating with a thin film of high-k materials such
as TiO2/ZrO2 to achieve high capacitance for high charge
delivery capacity. With 30-nm coating and 250 �
250 �m2 area, the coupling capacitance CEL is about
1.5 nF, one order of magnitude smaller than that of a
platinum electrode of same area. Total deliverable charge
per phase Qph can be expressed as

93. Qph ¼ ISTM � Tph ¼ CEL � VDD STM (4)
where ISTM is the stimulation current, Tph the time dura-
tion of the phase, and VDD STM the total voltage dynamic
excursion. Relatively low capacitance CEL can thus be
compensated by an increased total voltage excursion
VDD STM to deliver the required charge per stimulation
phase. In order to achieve Qph ¼ 10 nC per stimulation
phase, needed for effective neural stimulation under typi-
cal electrophysiological conditions, a dynamic voltage rail
with a total excursion of more than eight times the static
supply voltage VDDð¼ 0:8 VÞ is required.
Conventionally, this can be implemented by generating
the required high power supply voltages and supplying con-
stant currents from fixed power rails. However, drawing
currents in this manner incurs large energy penalties due
to the large voltage drop across the current source.

94. Instead, a much better way to perform stimulation is
to slowly ramp up the supply rails in an adiabatic fashion
to minimize the voltage drop across the current source.
Generation of the adiabatic voltage rails can be imple-
mented in various ways. External capacitors [89] or an
external inductor [90] can be employed. Alternatively,
pulse width control in rectifier can be used [199]. How-
ever, all of these methods have output ranges within the
LC tank swing voltages or VDD. Recently, on-chip charge
pumps are employed to generate a wide voltage excur-
sion for adiabatic stimulation [87]. Because this approach
utilizes the dc power supply as the input of charge
pumps, series of power efficiency loss cannot be avoided
in implantation settings. In addition, this method could

95. generate discrete levels of power supplies only, so the
energy losses due to the voltage drops across the current
source were considerable.
In contrast, ENIAC implements an adiabatic stimula-
tor that generates, at minimum energy losses, ramping
voltage power rails with greater than eight times the
voltage excursion of the LC tank, and with no need for
any external components. Consistent with the observa-
tions in Section II-A2, differential adiabatic stimulation
across a selected pair of electrodes is implemented, since
Fig. 13. Measured stimulation voltage and current waveforms
with platinum model electrode.
Fig. 12. (a) Simplified stackup of ENIAC showing an electrode
coated with high-k materials for capacitive interface.

96. (b) Principle of adiabatic stimulation with ENIAC. During the
first
phase, adiabatic voltage rails are generated directly from the LC
tank for energy-efficient stimulation. During the second phase,
the energy stored across the capacitive electrodes is replenished
for further energy savings.
26 Proceedings of the IEEE | Vol. 105, No. 1, January 2017
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the miniaturized and enclosed ENIAC system permits

97. no access to a distal ground electrode. As illustrated in
Fig. 12(b), the ENIAC stimulator operates in two
phases. During the first phase, constant complementary
currents are provided through the differential capacitive
electrodes. The ramping voltage adiabatic power rails
VDD STM and VSS STM providing the complementary cur-
rents are generated directly from the LC tank utilizing a
foldable stack of rectifiers. During the second phase en-
ergy is replenished by returning the charge stored on
the electrode capacitors to the system VDD and VSS for
reuse by other ENIAC modules. For triphasic rather
than biphasic stimulation, as shown, the two phases are
repeated but now with opposite polarity. This is accom-
plished by swapping the electrode connections through
the switch matrix prior to executing the same two-phase

98. sequence. Finally, the electrodes are shorted to even out
any residual charge on the electrode capacitors.
Fig. 13 shows measured voltage and current wave-
form for the triphasic stimulation with a platinum model
electrode, consistent with the model in Fig. 12(b), and
showing 145 �A of current delivered per electrode
channel. This is four times larger than other integrated
ECoG systems even though no external components
are used and system volume is substantially smaller
(Table 2).
VI. CONCLUDING REMARKS
In this work, we highlighted the importance of high den-

99. sity electrocorticography for brain activity mapping,
brain–computer interfaces, and treatments for neurologi-
cal disorders. We reviewed the critical design challenges
on fully implantable ECoG interface systems in their ma-
jor aspects including electrode interface, recording and
stimulation circuitry, wireless power and data communi-
cations. In addition, we surveyed state-of-the-art systems
at the forefront of clinical and research applications and
noted the rapid evolution of the technology in the past
few years. Finally, we make the case for a new type of de-
vice that promises to expand the applications of implant-
able brain monitoring: modular �ECoG. We demonstrate
a new approach to miniaturization of modular �ECoG
with our fully integrated encapsulated neural interfacing

100. acquisition chip (ENIAC). This system on a chip is capa-
ble of recording, stimulation, wireless power conditioning
and bidirectional communication without the need for
any external components. Its major specifications, perfor-
mances, and functionalities are summarized in compari-
son with other state-of-the-art ECoG interface systems in
Table 2. Having a fully integrated neural interface sys-
tem, including electrodes and antenna is a new milestone
for miniaturization that sets the stage for exciting clinical
and research developments. h
Table 2 Comparison of State-of-the-Art Wireless Integrated
ECoG Recording and Stimulation Systems
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Acknowledgment
The authors would like to thank V. Gilja, S. Dayeh,
E. Halgren, B. McNaughton, and J. Viventi for critical
input and stimulating discussions on clinical and funda-
mental neuroscience applications of high-density ECoG
neural interfaces.
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ABOUT THE AUTHORS
Sohmyung Ha (Member, IEEE) received the B.S.
(summa cum laude) and M.S. degrees in electrical
engineering from the Korea Advanced Institute
of Science and Technology (KAIST), Daejeon,
South Korea, in 2004 and 2006 and the M.S. and
Ph.D. degrees in bioengineering from the Univer-

154. sity of California San Diego, La Jolla, CA, USA, in
2015 and 2016, respectively.
From 2006 to 2010, he worked as an Analog
and Mixed-Signal Circuit Designer at Samsung
Electronics Inc., Yongin, Korea, where he was a part of the
engineering
team responsible for several of the world best-selling
multimedia de-
vices, smartphones and TVs. As of September 2016, he joins
New York
University Abu Dhabi, UAE, as an Assistant Professor in
Electrical Engi-
neering. His research aims at advancing the engineering and
applica-
tions of silicon-integrated technology interfacing with biology

155. in a
variety of forms ranging from implantable biomedical devices to
unob-
trusive wearable sensors.
Dr. Ha received the Fulbright Fellowship from 2010 to 2012,
and the
Engelson Ph.D. Thesis Award from the Department of
Bioengineering,
University of California San Diego, in 2016.
Abraham Akinin (Student Member, IEEE) was
born in Caracas, Venezuela. He received the B.S.
degree in biomedical engineering and physics
from the University of Miami, Coral Gables, FL,
USA, in 2010. He is currently working toward the

156. Ph.D. degree at the Bioengineering Department
and the Institute for Neural Computation, Univer-
sity of California San Diego, La Jolla, CA, USA.
His research interests include design of
closed-loop neuroprosthetics and biomedical in-
strumentation to restore sensory and cognitive function.
Jiwoong Park (Student Member, IEEE) received
the B.Sc. degree in electronic and computer
engineering from Hanyang University, Seoul,
South Korea, in 2012 and the M.S. degree in
electrical and computer engineering from the Uni-
versity of California San Diego (UCSD), La Jolla,
CA, USA, in 2014, where he is currently working

157. toward the Ph.D. degree.
His research interests include the design of
ultralow power body area network systems and
the design of miniaturized wireless power transfer systems for
biomed-
ical implants.
Chul Kim (Student Member, IEEE) received the
B.S. degree from Kyungpook National University,
Daegu, South Korea, in 2007 and the M.S. degree
from Korea Advanced Institute of Science and
Technology (KAIST), Daejeon, South Korea, in
2009, both in electrical engineering. He is
currently working toward the Ph.D. degree in

158. the Bioengineering Department, University of
California San Diego, La Jolla, CA, USA.
During 2009–2012, he worked for SK HYNIX
as a Power Circuitry Designer for DRAM. His research focuses
on
designing micropower integrated circuits and systems for
biomedical
applications and brain-machine interfaces.
32 Proceedings of the IEEE | Vol. 105, No. 1, January 2017
Ha et al.: Silicon-Integrated High-Density Electrocortical
Interfaces
Authorized licensed use limited to: University of Houston.
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159. Hui Wang (Student Member, IEEE) received the
B.Sc. degree in microelectronics and the S.M.
degree in circuits and systems from Shanghai
Jiao Tong University, Shanghai, China, in 2009
and 2012, respectively. He is currently working
toward the Ph.D. degree in electrical and com-
puter engineering at the University of California
San Diego (UCSD), La Jolla, CA, USA.
His research interests include the design of
fully integrated low-power acquisition platforms
for biomedical and implantable applications, ultralow-power
clock
generations, and energy-efficient mixed-signal integrated

160. circuits for
long-term environmental and medical monitoring systems.
Christoph Maier (Member, IEEE) received the
Diplom-Physiker degree from the University of
Heidelberg, Heidelberg, Germany, in 1995 and
the Dr.sc.techn. degree in electrical engineering
from the Swiss Federal Institute of Technology
Zurich, Zurich, Switzerland, in 2000.
After time in industry, he joined the Inte-
grated Systems Neuroengineering Laboratory,
University of California San Diego, La Jolla, CA,
USA, as a Postdoctoral Researcher in 2010. His
main research interests are interfaces for electrophysiological

161. signals
and modeling neural networks in analog VLSI.
Patrick P. Mercier (Member, IEEE) received the
B.Sc. degree in electrical and computer engineer-
ing from the University of Alberta, Edmonton,
AB, Canada, in 2006 and the S.M. and Ph.D.
degrees in electrical engineering and computer
science from the Massachusetts Institute of Tech-
nology (MIT), Cambridge, MA, USA, in 2008 and
2012, respectively.
He is currently an Assistant Professor in Elec-
trical and Computer Engineering at the Univer-
sity of California San Diego (UCSD), La Jolla, CA, USA, where

162. he is also
the Co-Director of the Center for Wearable Sensors. His
research inter-
ests include the design of energy-efficient microsystems,
focusing on
the design of RF circuits, power converters, and sensor
interfaces for
miniaturized systems and biomedical applications.
Prof. Mercier received a Natural Sciences and Engineering
Council of
Canada (NSERC) Julie Payette fellowship in 2006, NSERC
Postgraduate
Scholarships in 2007 and 2009, an Intel Ph.D. Fellowship in
2009, the
2009 ISSCC Jack Kilby Award for Outstanding Student Paper at
ISSCC

163. 2010, a Graduate Teaching Award in Electrical and Computer
Engineer-
ing at UCSD in 2013, the Hellman Fellowship Award in 2014,
the Beck-
man Young Investigator Award in 2015, the DARPA Young
Faculty
Award in 2015, and the UCSD Academic Senate Distinguished
Teaching
Award in 2016. He currently serves as an Associate Editor of
the IEEE
TRANSACTIONS ON BIOMEDICAL CIRCUITS AND
SYSTEMS and the IEEE TRANSAC-
TIONS ON VERY LARGE SCALE INTEGRATION, and is
coeditor of Ultra-Low-
Power Short-Range Radios (New York, NY, USA: Springer-
Verlag, 2015),
and Power Management Integrated Circuits (Boca Raton, FL,

164. USA: CRC
Press, 2016).
Gert Cauwenberghs (Fellow, IEEE) received the
M.Eng. degree in applied physics from the Uni-
versity of Brussels, Brussels, Belgium, in 1988
and the M.S. and Ph.D. degrees in electrical engi-
neering from the California Institute of Technol-
ogy, Pasadena, CA, USA, in 1989 and 1994,
respectively.
Currently, he is a Professor of Bioengineering
at the University of California San Diego, La Jolla,
CA, USA, where he codirects the Institute for
Neural Computation, participates as a member of the Institute of

165. Engi-
neering in Medicine, and serves on the Computational
Neuroscience Ex-
ecutive Committee of the Department of Neurosciences graduate
program. Previously, he was Professor of Electrical and
Computer Engi-
neering at Johns Hopkins University, Baltimore, MD, USA and
Visiting
Professor of Brain and Cognitive Science at the Massachusetts
Institute
of Technology, Cambridge, MA, USA. He cofounded and chairs
the Sci-
entific Advisory Board of Cognionics Inc. His research focuses
on micro-
power biomedical instrumentation, neuron–silicon and brain–
machine

166. interfaces, neuromorphic engineering, and adaptive intelligent
systems.
Dr. Cauwenberghs received the National Science Foundation
Career
Award in 1997, the Office of Naval Research (ONR) Young
Investigator
Award in 1999, and the Presidential Early Career Award for
Scientists
and Engineers in 2000. He was Francqui Fellow of the Belgian
Ameri-
can Educational Foundation. He was a Distinguished Lecturer of
the
IEEE Circuits and Systems Society in 2002–2003. He served
IEEE in a
variety of roles including as General Chair of the IEEE
Biomedical Cir-
cuits and Systems Conference (BioCAS 2011, San Diego), as

167. Program
Chair of the IEEE Engineering in Medicine and Biology
Conference
(EMBC 2012, San Diego), and as Editor-in-Chief of the IEEE
TRANSACTIONS
ON BIOMEDICAL CIRCUITS AND SYSTEMS.
Vol. 105, No. 1, January 2017 | Proceedings of the IEEE 33
Ha et al.: Silicon-Integrated High-Density Electrocortical
Interfaces
Authorized licensed use limited to: University of Houston.
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FULL PAPER
1700625 (1 of 9) © 2018 The Authors. Published by WILEY-

185. VCH Verlag GmbH & Co. KGaA, Weinheim
www.advancedscience.com
Nanofabricated Ultraflexible Electrode Arrays for
High-Density Intracortical Recording
Xiaoling Wei, Lan Luan, Zhengtuo Zhao, Xue Li, Hanlin Zhu,
Ojas Potnis,
and Chong Xie*
Dr. X. Wei, Dr. L. Luan, Z. Zhao, X. Li, H. Zhu, O. Potnis,
Prof. C. Xie
Department of Biomedical Engineering
The University of Texas at Austin
Austin, TX 78712, USA
E-mail: [email protected]
Dr. L. Luan
Department of Physics
The University of Texas at Austin
Austin, TX 78712, USA
DOI: 10.1002/advs.201700625
epilepsy,[15] and obsessive compulsive dis-

186. order.[12] Moreover, they allow for direct
communication between brain and man-
made devices, which can enable appli-
cations such as human brain–machine
interface and neuroprosthetics.[16,17] How-
ever, these conventional neural electrical
probes typically have dimensions substan-
tially larger than neurons and capillaries,
which fundamentally precludes the possi-
bility of interrogating the whole neuronal
population in a functional brain region.
In particular, microwire electrodes,[3] tet-
rodes,[4] and Utah array[18] host only one
recording site at the tip of each wire, and
therefore cannot simultaneously record
neural activity at multiple depths. Micro-
electromechanical system-based silicon
probes[19,20] have significantly increased
the number of recording sites on one
probe. However, these silicon probes typi-
cally have cross-sectional areas around
or greater than 103 µm2, which gives the
volume per electrode similar to that of the microwire and tet-
rodes, all at two orders of magnitude larger than the average

187. size of a neuronal soma (Table S1, Supporting Information).
In addition, because these probes are strongly invasive to
living brain tissue,[21,22] to maintain tissue vitality their
highest
implantation density is limited by only allowing at most 1–2%
of the enclosed volume to be occupied by the electrode
array.[23]
Therefore, the smallest interprobe spacing is limited to be at
least several hundred micrometers for both microwire array
and silicon probes.[24] Although flexible neural electrodes are
generally believed to induce less tissue reaction than their
rigid counterparts, their highest implantation densities demon-
strated to date are still at similar values.[25,26]
Previous efforts have demonstrated that smaller electrodes
composed of both novel[27,28] and conventional materials[29]
resulted in suppressed inflammatory responses that can poten-
tially support higher density implantation than larger elec-
trodes. In the effort of increasing packing density on one neural
probe, advanced lithography techniques such as electron-beam
lithography (EBL) have been used to fabricate silicon-based
microelectrodes that enable closely packed recording sites
along the length of the shank.[30–33] However, despite great
capacity and potential, these nanofabricated probes based
on conventional rigid architectures are known to elicit sig-

188. nificant reactions by the host tissue, leading to challenges in
high-density implantation over chronic time scale.[31] Recently
Understanding brain functions at the circuit level requires time-
resolved
simultaneous measurement of a large number of densely
distributed neu-
rons, which remains a great challenge for current neural
technologies. In
particular, penetrating neural electrodes allow for recording
from individual
neurons at high temporal resolution, but often have larger
dimensions than
the biological matrix, which induces significant damage to brain
tissues and
therefore precludes the high implant density that is necessary
for mapping
large neuronal populations with full coverage. Here, it is
demonstrated
that nanofabricated ultraflexible electrode arrays with cross-
sectional areas
as small as sub-10 µm2 can overcome this physical limitation.
In a mouse
model, it is shown that these electrodes record action potentials
with high

189. signal-to-noise ratio; their dense arrays allow spatial
oversampling; and their
multiprobe implantation allows for interprobe spacing at 60 µm
without elic-
iting chronic neuronal degeneration. These results present the
possibility of
minimizing tissue displacement by implanted ultraflexible
electrodes for scal-
able, high-density electrophysiological recording that is capable
of complete
neuronal circuitry mapping over chronic time scales.
Electrodes
1. Introduction
Implanted neural probes[1,2] such as microwires,[3] tetrodes,[4]
and silicon-based microelectrodes[5–7] are among the most
important techniques in both fundamental and clinical neu-
roscience. Scientifically, they remain our only option to tem-
porally resolve the fastest electrophysiological activities of
individual neurons, which provides critical information to
dissect the neural circuitry.[8–11] Clinically, neural electrodes
have been successfully used in the treatment for a number
of neurological disorders[12,13] such as Parkinson’s

190. disease,[14]
© 2018 The Authors. Published by WILEY-VCH Verlag GmbH
& Co. KGaA,
Weinheim. This is an open access article under the terms of the
Creative
Commons Attribution License, which permits use, distribution
and re-
production in any medium, provided the original work is
properly cited.
Adv. Sci. 2018, 5, 1700625
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/
www.advancedsciencenews.com
1700625 (2 of 9) © 2018 The Authors. Published by WILEY-
VCH Verlag GmbH & Co. KGaA, Weinheim
www.advancedscience.com
developed ultraflexible nanoelectronic neural probes,[34–37]

191. using
a substrate-less, multilayer layout, markedly reduced the cross-
sectional area to the subcellular range,[36] but demonstrated
limited electrode density along the probe due to the fabrica-
tion resolution of the planar photolithography techniques.
In this paper, we combined the unconventional ultraflexible
device architecture with state-of-the-art EBL to fabricate the
nanoelectronic thread (NET-e) probe with densely packed elec-
trode arrays and versatile design patterns. We fabricated neural
probes each hosting eight or more electrodes with substantially
reduced cross-sectional areas at below 10 µm2. Taking advan-
tage of sub-10 µm shuttle devices, we further demonstrated
implantation of NET-e probes with interprobe spacing as small
as 60 µm, which overcame the dimensional limits of electrodes
for high-density neural mapping.
2. Results and Discussion
We adopted a hybrid method involving both electron beam
and optical lithography[38] to fabricate NET-e devices with
high
throughput (detailed fabrication procedures in Section S1 and
Figure S1 in the Supporting Information). We used EBL to
define the implanted section (the “thread”) where dimension

192. constraints were stringent and used photolithography with
relaxed resolution requirement for larger structures that were
not implanted into brain tissue (an example of EBL and photo-
lithography sections are shown in Figure S2 in the Supporting
Information). Figure 1a–f shows the overview and zoom-in
images of the as-fabricated EBL section on NET-e probes with
different patterns of electrode arrays. NET-e-l (Figure 1a)
hosted
a linear array of electrodes with a cross-sectional profile of
0.8 µm × 8 µm, the smallest among all reported electrodes to
our
knowledge (Figure S3 and Table S1, Supporting Information).
NET-e-t (Figure 1b) was designed to function similarly as mul-
tiple tetrodes spanning across the cortical depth, hosting groups
of four closely spaced electrodes every 100 µm along the
thread.
NET-e-o (Figure 1c) had a continuum of individually addressed
electrodes along the thread. For improved single-unit detec-
tion and sorting yield, both NET-e-t and NET-e-o were designed
to have electrode spacing smaller than their detection range to
enable spatial oversampling in action potential recording. To
minimize NET-e probe’s cross-sectional area, we designed a
multilayer architecture with no substrate similar to previous
Adv. Sci. 2018, 5, 1700625

193. Figure 1. As-fabricated NET-e devices. a–c) Photographs of the
EBL section of a variety of NET-e devices, including a) linear
array NET-e-l, b) over-
sampling array NET-e-o, and c) tetrode-like array NET-e-t. d–f
) Zoom-in images of panels (a)–(c) as marked by the dashed
boxes, showing the fine
structure and precise interlayer alignment. g) Sketch of the two
multilayer architecture of the NET-e devices. h) Scanning
electron microscopy (SEM)
image (top) and height profile by atomic force microscopy
(AFM, bottom) of an NET-e-t electrode showing the sub-
micrometer thickness and fine
layered structures at the cross section. Dashed line marks the
position of the AFM height measurement. Scale bars: 50 µm,
panels (a)–(c); 10 µm,
panels (d–f ); and 5 µm, panel (h).
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1700625 (3 of 9) © 2018 The Authors. Published by WILEY-
VCH Verlag GmbH & Co. KGaA, Weinheim

194. www.advancedscience.com
work,[36] where interconnect traces and electrodes were
fabricated on different layers separated by insulating layers
(Figure 1g,h). To facilitate the transition from EBL to optical
lithography and to avoid disconnected interconnects and insu-
lating layers, we intentionally overlapped the sections of EBL
and photolithography by at least 4 µm in all directions in all
layers as shown in Figure S2 (Supporting Information).
We successfully overcame several technical challenges in
the fabrication of NET-e probes, including precise interlayer
alignment across the entire wafer, and the application of SU-8
as a sensitive negative e-beam resist to construct the insula-
tion layers (discussed in details in the following sections). We
achieved a minimum linewidth of 200 nm (pitch of 400 nm) for
the interconnect traces and sub-100 nm interlayer alignment.
The total thickness of the NET-e probes was determined mostly
by the thickness of the two SU-8 layers, and was precisely con-
trolled to be 0.8–1 µm (Figure 1h) by fine tuning the e-beam
exposure dose of SU-8 with 0.5 µC cm−2 accuracy (Figure S4,
Supporting Information).
The sub-1 µm thickness led to ultraflexibility that precludes
free standing in air after being released from the substrates.

195. Figure 2a–e shows the entire device (Figure 2a), the flexible
section (Figure 2b), and the EBL sections of the NET-e probes
(Figure 2c–e) floating in water after released. Because the
ultraflexible NET-e devices could not self-support for insertion
into brain tissue, we used a microshuttle device and a “needle-
and-thread” strategy to deliver the NET-e probes to the desired
location and depth in the mouse brain. Similar to the delivery
of larger NETs we recently reported,[36] the temporary engage-
ment during delivery was enabled by a micropost machined
at the end of the shuttle device fitting into a microhole fab-
ricated at the end of the NET-e probe (Figure S5, Supporting
Information). Significantly different from their larger counter-
parts,[36] the much reduced dimension of the NET-e imposed a
more stringent requirement on the shuttle device dimension.
The ultrasmall NET-e probes were only successfully deliv-
ered using shuttle devices with diameters smaller than 10 µm
(Figure 2f,g), whereas for shuttle devices with larger diameters,
the capillary force when retracting the shuttle device was suf-
ficiently large to drag out the NET-e probe. We attribute it to
the much-larger-than-probe acute tissue displacement during
implantation, which led to insufficient friction force by the
surrounding tissue to retain the probe in place during shuttle
device retraction, a failure mode that was previously reported
for larger shuttle devices and flexible neural probes.[39] The

196. use
of microshuttle devices and NET-e smaller than 10 µm brought
Adv. Sci. 2018, 5, 1700625
Figure 2. Implantation of the NET-e devices. a–e) Photograph
of NET-e devices immersed in water: a) overview of an NET-e
device including the car-
rier chip and a connector to external I/O mounted atop; b) the e-
beam section of four threads on panel (a) showing the
ultraflexibility; c–e) electrodes
on c) NET-e-l, d) NET-e-t, and e) NET-e-o. f ) Pseudocolor
SEM image of an NET-e-l device (in purple and gold) attached
on a shuttle device made of
carbon fiber (purple) with a diameter of 7 µm, showing the
small dimension of both. g) Zoom-in view in panel (f ). h)
Photograph showing an NET-
e-o probe successfully delivered into living mouse brain.
Arrows denote the delivery entry sites. Dashed lines mark the
probe implanted beneath the
brain surface. Image was taken 20 d post implantation through a
cranial optical window. i) Photograph of a mouse immediately
after NET-e probe
implantation. Gently pulling the carrier chip straightened the
relaxed section of the probe without pulling out the implanted

197. section. j) Photograph of
a head-constrained mouse on a custom-built treadmill for awake
recording. Scale bars: 2 mm, panel (a); 200 µm, panels (b,h); 20
µm, panels (c–f );
and 10 µm, panel (g).
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1700625 (4 of 9) © 2018 The Authors. Published by WILEY-
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www.advancedscience.com
down the surgical footprints to subcellular dimensions, which
formed tight tissue integration immediately after implantation
in which the NET-e remained embedded in brain tissue even
when gently pulled (Figure 2h,i). We implanted NET-e devices
into somatosensory cortex in the mouse brain at targeted depths
of 500–700 µm at the tip so that the electrodes along the probe
span over the cortical depth. The implantation yield was 60%
(n = 15). In vivo recording was typically performed at
1–2 months after implantation. Taking advantage of the
ultraflex-

198. ibility and the ultrasmall dimensions, we accommodated chronic
optical access concurrently with NET-e implants (Figure 2j)
similarly as we recently demonstrated.[36]
The electrodes on NET-e probes were finished by gold thin
film and had dimensions ranging from 5 × 8 to 12 × 15 µm2
depending on the design. The impedance at 1 kHz ranged
between 1 and 1.8 MΩ, larger than their photolithography-
defined
counterparts,[36] which is consistent with previous results[40]
(Figure 3a). While reducing the dimensions of the neural elec-
trodes is beneficial for recording density and specificity, as
well as tissue compatibility, it leads to the increase of imped-
ance and therefore the decrease of signal-to-noise ratio (SNR).
Conducting polymers such as poly(3,4-ethylenedioxythiophene)
(PEDOT),[41–47] poly(pyrrole),[48] nanomaterials such as
carbon
nanotubes,[49] silicon nano wires,[50] and polymer
nanotubes,[51]
and other functional materials such as IrOx
[1,52] have been used
to decrease electrode impedance and increase charge injec-
tion capacity. We electrochemically deposited PEDOT on the

199. NET-e electrodes with an area of 12 × 15 µm2 and observed the
impedance decreased from 0.87 ± 0.33 to 0.046 ± 0.026 MΩ
(Figure 3a, dashed box). The decrease of impedance is expected
to improve the SNR and suppress low-frequency artifact.[51] In
the rest of this work, we only used electrodes without coating
for consistency, whose relatively large impedance combined
with animal motion during awake measurements led to slightly
elevated noise levels, 11.4 ± 1.7 µV (Figure 3b), but all these
electrodes were capable of detecting and isolating single-unit
action potentials. Figure 3c shows the SNR of n = 10 electrode
sites that yielded detectable spikes in three probes implanted
in three mice for 8 weeks. Figure 3d–f shows the representa-
tive recordings, the SNR, and the spike waveforms that were
isolated from the 10 min recording session from three different
electrodes at 2 weeks (Figure 3d) and 5 weeks (Figure 3e,f).
The
recording in Figure 3d had well-isolated single units and was
among the best in signal quality with SNR of 19.0 and a noise
Adv. Sci. 2018, 5, 1700625
Figure 3. In vivo recording performance of NET-e devices. a) In
vivo impedance at 1 kHz measured at 1week after implantation,
n = 20 for each dimen-
sion. Dashed box: impedance of electrodes with PEDOT coating

200. (n = 6) measured in 1 × PBS. b) In vivo noise level of the
smallest electrode (5 µm ×
8 µm) at anesthetized (right, 6.46 µV median) and awake (left,
11.4 µV median) measurements (bandwidth: 0.5 Hz to 7.5 kHz),
n = 20. Measurement
time: 5 weeks after implantation. c–f ) SNR of action potential
recordings by NET-e probes (n = 10 electrode sites of three
NET-e-l probes in three anes-
thetized mice) over c) 8 weeks and representative recordings
from three implanted NET-e-l electrodes in an anesthetized
mouse d) 2 weeks and e,f )
5 weeks after implantation. Left: 1s real-time recording trace;
250 Hz high-pass filter applied. Right: Superimposed spikes
isolated from the recording
traces. All unit events were plotted in light gray and averaged
waveforms plotted in red and blue. SNR was calculated using
the larger waveform when
there were two recorded on one electrode. Vertical scale bars:
50 µV, panels (c–e); horizontal scale bars: 0.1 s, panels (c–e,
left) and 0.25 ms (c–e, right).
The symbols * and ** in panels (a,b) denote significant
difference of p < 0.01 and p < 0.001 between the groups,
respectively.

201. www.advancedsciencenews.com
1700625 (5 of 9) © 2018 The Authors. Published by WILEY-
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www.advancedscience.com
level of 7.63 µV. The recording in Figure 3f had discriminable
spike clusters, but was among the worst in signal quality with
an SNR of 8.6 and an average noise level of 6.36 µV. The SNR
is slightly lower but on par with that from its microfabricated
counterparts[36] and other conventional bare electrodes without
polymer coating,[51,53] which is consistent with their reduced
electrode dimension.
Figure 4 shows the representative recording from NET-e-l
electrodes and NET-e-t electrodes, performed 5 and 9 weeks
after implantation, respectively. We used the adjacent
electrodes
in the two designs to estimate their spike detection range,
which depends on its impedance, dimension, and geometry.
The detection range is a crucial parameter to determine the
necessary electrode density for achieving complete detection
of all enclosed neurons within a brain region.[8,23] As shown in

202. Figure 4b,c, on NET-e-l electrodes that were separated by 80
µm
along the thread, the same spikes were typically picked up by
adjacent recording sites with strongly attenuated amplitudes,
suggesting that the detection range is comparable to the elec-
trode separation. Consistent with this detection range, the NET-
e-t electrodes recorded temporally correlated spikes from the
four “tetrode-like” electrodes in one group which were densely
packed within an area of 18 µm × 12 µm (Figure 4d,e). The
spatial–temporal correlation of the four electrodes improved
the detection and sorting fidelity of single-unit action potentials
(Figure 4e) analogous to tetrodes.[4] In addition, this scalable
architecture enabled simultaneous detection at multiple brain
depths.
Our previous publication[36] demonstrated that microfabri-
cated NETs afford stable recording and nondegrading tissue–
probe interface in mice through a collection of comprehensive
characterizations. In this work, we did not include systematic
studies on the chronic performance of NET-e probes because
they share similar materials, structures, and the ultraflexibility.
We therefore expect NET-e probes have comparable tissue–
probe interface and recording stability as previously demon-
strated NETs. In the recording period of this study (2 months),
we did not observe chronic deterioration in recording perfor-

203. mance (Figure 4f). Similar to the observation of larger
NETs,[36]
in vivo two-photon (2P) imaging 2 months after implantation
showed normal neuronal density (Figure 5a) and morphology
of vasculature (Figure 5b) near the NET-e electrodes. Impor-
tantly and advantageous to the previous larger NET devices, the
much reduced lateral dimensions of NET-e probes allowed for
multiprobe implantation at a previously unattainable high den-
sity. Figure 5c shows the photograph of living brain immedi-
ately after implantation of seven NET-e probes, where the inter-
probe distance was as small as 60 µm, a distance significantly
smaller than the pitch of current state-of-the-art microelec-
trode arrays[31] and would allow electrodes on the two adjacent
probes to have overlapping detection range. We examined post-
mortem tissue–probe interface at 4 months after implantation
(Figure 5d–f). The bright-field images (Figure 5d,e) and the flu-
orescent images from immunohistological staining (Figure 5f)
revealed healthy tissue morphology and normal neuronal den-
sity, suggesting that the long-term presence of NET-e device
Adv. Sci. 2018, 5, 1700625
Figure 4. Representative unit recording from NET-e electrodes.
a) Schematic showing the relative position of electrodes on
NET-e-l (left) and NET-e-t

204. (right) devices in the brain. b) Recording time trace from an
NET-e-l probe hosting a linear electrode array. Inset sketches
the NET-e-l probe. c) 10 ms
recording trace from panel (b) on the top four electrodes
highlighting a single spike that was picked up by adjacent
electrodes with attenuated ampli-
tudes. Same color code as panel (b). b,c) The recording was
performed 5 weeks after implantation. d) Recording time trace
from a group of four
NET-e-t electrodes showing correlated spikes. Inset sketches
the NET-e-t probe. e) Sorted single-unit waveforms from panel
(d). d,e)The recording
was performed 9 weeks after implantation. f ) Superimposed
spikes isolated from the same NET-e-l electrode over 8 weeks
post implantation. Aver-
aged waveforms shown in red. Vertical scale bars: 50 µV,
panels (b–f ); horizontal scale bars: 0.1 s, panels (b) and (d); 2
ms, panel (c); and 0.5 ms,
panels (e,f ).
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arrays at high packing density did not result in “dead” zone
near the electrodes. This is also consistent with our previous
study where an unintentionally folded NET probe at a curvature
of about 50 µm in living brain did not elicit astrocytes accumu-
lation at 3.5 months postimplantation.[36]
Different components of the NET-e probes require different
EBL techniques to construct, which were individually optimized
for high yield, throughput, and fabrication resolution. Standard
EBL using poly(methyl methacrylate) (PMMA) as the positive
resist followed by metallization and lift-off was performed to
define the interconnects and the electrodes. The highest resolu-
tion was required for interconnect traces, which is determined
by the EBL sample stage stitching error, because the length of
the thread (about 1 mm) was much larger than the size of the
writing field in the EBL tool used in this work (80 µm). Non-
standard EBL was performed to construct the insulation layers,
for which we tested both PMMA[54] and SU-8[55] as negative
EBL resists. These two resists were chosen for their good insu-
lation, excellent tensile strength after hard baking, and tun-

206. able thickness depending on the exposure doses.[54,55] Because
PMMA requires a high dose of about 40 000 µC cm−2 to cross-
link[54] that leads to prohibitively long EBL exposure times
even
at high currents, we chose to use SU-8 for higher fabrication
throughput.
Our fabrication yield was about 70% with little variation
among different design patterns that required fabrication reso-
lution ranging between 200 and 400 nm. We have thoroughly
examined our fabrication process and identified that the yield
loss is mainly due to fabrication defects. All fabrication proce-
dures, including four EBL steps, seven photolithography steps,
and five metal deposition steps, were performed manually,
which make it difficult to completely avoid microparticles and
scratches especially on the long, narrow interconnect traces.
The
EBL exposure time was relatively short. For example, an entire
4″
wafer that contains 8 NET-e devices and a total of 256
electrodes
typically requires exposure times of ≈5 min for the insulating
layers composed of SU-8, 2 h for the interconnects, and 15 min
for the electrode layers. However, the fabrication throughput
was mostly limited by the two-step iterative alignment pro-

207. cess during EBL that was necessary for sub-100 nm registra-
tion accuracy across the entire wafer. Despite the relatively low
throughput compared with photolithography, the application of
EBL greatly improved the fabrication resolution and reduced the
probe’s overall dimensions, and further offers great flexibility
in
patterning nanoscale structures by design. We are confident that
the fabrication yield, throughput, and resolution can be further
improved using more advanced EBL equipment.
Ultraflexibility requires additional efforts in careful han-
dling and implantation. However, ultraflexibility is necessary
to achieve stable chronical recording and nondegrading tissue–
probe interface.[36] Significantly different from our previous
work that focused on the chronically stable recording and non-
degrading tissue–probe interface, this work focused on pushing
the dimensional limit of ultraflexible neural electrodes on a
similar device architecture. Using nanofabrication and refined
implantation techniques, we further reduced both the device
cross-sectional area and the surgical footprints per recording
site by an order of magnitude. Importantly and advantageous to
the previous larger NET devices, the much reduced dimensions
of NET-e probes allowed for multiprobe implantation at a pre-
viously unattainable high density (e.g., 60 µm interprobe dis-
tance) without eliciting observable neuronal death over chronic

208. implantation. The capability of fabricating scalable, subcellular-
sized electrodes with an ultraflexible architecture represents, in
our opinion, a crucial first step toward long-term, full-coverage
mapping of the neural activity in a sizable brain region.
3. Conclusion
In this study, we demonstrated the possibility of integrating
neural electrode arrays within a subcellular form factor and
their implantation in a high-density, scalable manner. By
Adv. Sci. 2018, 5, 1700625
Figure 5. In vivo and postmortem tissue–probe interface. a)
Reconstruc-
tion of in vivo 2P images of neurons (yellow, Thy1-YFP)
surrounding
an NET-e-l probe (red) 2 months post implantation. Image
stack:
100–320 µm below the brain surface. b) 3D reconstruction of
vasculature
by in vivo 2P microscopy around NET-e-l probe (red) 2 months
post-
implantation, showing normal capillary networks (green). Image
stack:

209. 100–320 µm below the brain surface. c) Photograph showing in
vivo
implantation of multiple NET-e-l probes. Arrows denote the
implantation
locations. d) Bright field image of a postmortem tissue slice at
the probe–
tissue interface as shown in panel (c) 4 months
postimplantation. Arrows
denote the probes. e) Zoom-in bright field image of the boxed
region
shown in panel (d). Arrows denote the probes. f ) Fluorescence
image of
the same area as in panel (e). Color code: yellow, NeuN,
labelling neuron
nuclei; Rhodamine 6G, labelling NET-e probe. Normal neuronal
density
was observed near the two probes at inter-probe distance was 60
µm.
Scale bars: 50 µm, panels (a–d); 10 µm, panels (e,f ).
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Adv. Sci. 2018, 5, 1700625
applying nanofabrication techniques on unconventional sub-
strate-less design of neural probes, we drastically reduced the
physical dimensions of neural probes. Combining these nano-
fabricated ultraflexible probes with minimally invasive implan-
tation methods at subcellular surgical footprints, we developed
a practical approach to overcome current physical limits
in the design and implantation of intracortical neural elec-
trodes, which paves the road for chronic, full-coverage, neural
recording and complete circuit-level mapping of neural activity.
4. Experimental Section
Shuttle Device Fabrication and Assembly: A straight segment of
carbon
fiber was attached to a stainless steel microneedle (prod#
13561-10,
Ted Pella, Inc.) for convenient handling. It was then cut to the
designed
length (2–3 mm) using focused ion beam (FIB). An anchor post

211. was
micromilled at the tip of the shuttle device using FIB to shape a
well-
defined micropost (≈3 µm in diameter, 4 µm in height).
Electrochemical Deposition of PEDOT: PEDOT deposition was
carried out in 3,4-ethylenedioxythiophene monomer (EDOT)
(0.07 m)
and KNO3 (0.13 m) aqueous solution. The electrochemical
deposition
was performed by a WaveNano USB Potentiostat (Pine Research
Instrumentation) via its interface software, the AfterMath, at
room
temperature. PEDOT was deposited in potentiostatic mode at
0.8 V, with
a two-electrode configuration. The working electrodes were
connected
to the electrode site. The reference electrode (Ag/AgCl
electrode) and
counter electrode were connected to a platinum wire in contact
with the
EDOT/ KNO3. The polymerization time was typically set to 20
s and can
be varied to control the coating thickness.

212. Animals and Surgery: Wild-type male mice (C57BJ/6, 8 weeks
old, Taconic)
were used in the experiments. Mice were housed at the Animal
Research
Center, UT Austin (12 h light/dark cycle, 22 °C, food and water
ad libitum).
Mice were anesthetized using isoflurane (3% for induction and
maintained at 1–2%) in medical-grade oxygen. The skull was
exposed
and prepared by scalping the crown and removing the fascia,
and then
was scored with the tip of a scalpel blade. A 3 mm × 3 mm
square
craniotomy was performed with a surgical drill over the
somatosensory
cortex. Dura mater was carefully removed to facilitate the
delivery.
After NET-e probe implantation (described in the following
section),
the remaining flexible segment of the NET probe, which
connected the
bonding pad on the substrate with the electrodes inside the
brain, was
routed to the edge of the cranial opening. The exposed brain

213. was then
protected by artificial cerebrospinal fluid (ACSF) and a
coverslip #1
(Fisher Scientific) fit into the cranial opening. The space
between the
coverslip and the remaining skull was filled with Kwik-sil
adhesive (World
Precision Instruments). After the skull was cleaned and dried, a
layer of
low-viscosity cyanoacrylate was applied over the skull. An
initial layer of
C&B-Metabond (Parkell Inc.) was applied over the
cyanoacrylate and the
Kwik-sil. A second layer of Metabond was used to cement the
coverslip
and the NET carrier chip to the skull. All procedures complied
with the
National Institute of Health guidelines for the care and use of
laboratory
animals and were approved by the University of Texas
Institutional
Animal Care and Use Committee.
Neural Probe Implantation—Insertion: In typical procedures, a
flexible

214. section of NET-e probe was placed on the brain surface where
dura mater
was removed. The shuttle device was mounted on a
micromanipulator
(MP-285, Shutter instrument) vertically, and positioned atop the
engaging hole at the end of the probe. As the shuttle device
traveled
downward, the anchor post entered into the hole and pulled the
neural
probe into the brain tissue. Once the neural probe reached the
desired
depth, the shuttle device was retracted, and the neural probe
was
released and left embedded in the brain tissue.
Two-Photon Imaging: 2P imaging was performed in a laser
scanning
microscope (Praire Technology) equipped with a 20× water
immersion
objective (NA 1.0; Zeiss) and a Ti:sapphire excitation laser
(MaiTai DS,
Spectra-Physics) at a few weeks to months postimplantation.
The laser
was tuned to 810–910 nm for 2P excitation (power: 3.0–50 mW,

215. dwell
time: 4.0–6.0 µs). Fluorescence emissions were detected
simultaneously
by two standard photomultiplier tubes with a 595/50 nm filter
(Semrock,
US) for “red” fluorescence emission and a 525/70 nm filter
(Semrock,
US) for “green” fluorescence emission. Mice were anesthetized
using
isoflurane (3% for induction and 1.5% during experiment) in
medical-
grade oxygen to maintain full immobility during imaging and
placed in
a frame that stabilized the head on the microscope stage.
Anesthetized
animals were given fluorescein isothiocyanate (FITC)-dextran
(0.1 mL,
5% w/v, Sigma) retro-orbitally to label blood vessels prior to
imaging. To
facilitate imaging the probe–tissue interface beyond superficial
cortical
layers, the NET-e probes were doped with sulforhodamine 6G
(Sigma) in
the insulating layers and delivered at about 45° with respect to
the skull.

216. Awake Electrophysiological Recording: Mice were allowed a 1–
2 week
recovery period after surgery. For recording, mice were head
constrained
on a custom-made air-supported spherical treadmill to allow
walking and running, similar to the setup previously used for
optical
imaging.[44] The treadmill was made of an 8″ diameter
Styrofoam ball
(Floracraft) levitated by a thin cushion of air between the ball
and a
casting containing air jets. Voltage signals from the NEC
devices were
amplified and digitized using a 32-channel RHD 2132
evaluation system
(Intan Technologies) with a bare Ag wire inserted into the
contralateral
hemisphere of the brain as the grounding reference. The
sampling
rate was 20 kHz, and a 300 Hz high-pass and a 60 Hz notch
filter were
applied for single-unit recording. Mice and the amplifier were
placed
in a noise-attenuated, electrically shielded chamber. Impedance

217. of the
recording electrodes was measured using the same setup at 1
kHz prior
to recording.
Histological Sample Preparation: Mice were given lethal
intraperitoneal
injections of 0.15 mL ketamine mixed with xylazine (10 mg
mL−1
xylazine in 90 mg mL−1 ketamine) and then perfused
intracardially
with oxygenated, cold (≈4 °C) modified ACSF (2.5 × 10−3 m
KCl,
1.25 × 10−3 m NaH2PO4, 25 × 10−3 m NaHCO3, 0.5 × 10−3 m
CaCl2,
7 × 10−3 m MgCl2, 7 × 10−3 m dextrose, 205.5 × 10−3 m
sucrose,
1.3 × 10−3 m ascorbic acid, and 3.7 × 10−3 m pyruvate)
followed by 4%
paraformaldehyde in 0.02 m phosphate-buffered saline (PBS).
Brains
were cryoprotected in a 30% sucrose/4% paraformaldehyde
solution
overnight. Tissue was sectioned into 20–50 µm slices
perpendicular to

218. the probe using a Leica CM1950 cryostat (Leica Microsystems).
The
slices were washed (3 × 5 min) and incubated in hot sodium
citrate
solution (850–950 °C, 0.01 m in H2O) for 30 min for antigen
retrieval.
Then, the slices were washed (3 × 5 min), incubated in blocking
solution
and permeabilized (0.5% Triton X and 10% normal goat serum
(Sigma)
in PBS) for 3 h at room temperature, washed (4 × 5 min), and
incubated
in fluorophore conjugated antibodies for 24 h at 4 °C. Reagents
used
for Neurons were (Millipore): Alexa Fluor 488 conjugated anti-
NeuN
antibody, clone A60.
Statistical Analysis: Standard statistical analysis was performed
using Matlab to calculate the average and standard deviation for
the
impedance, SNR, noise amplitude for the recording data.
Analysis
of variance (ANOVA) was done on the impedance with different
electrode sizes after implantation. Independent t-test was

219. performed
on the results of noise amplitude for awake and anesthetized in
vovo
recording (Matlab).
Supporting Information
Supporting Information is available from the Wiley Online
Library or
from the author.
Acknowledgements
X.W., L.L., and Z.Z. contributed equally to this work. The
authors thank the
Microelectronics Research Center at UT Austin for the
microfabrication
facility and support, and the Animal Resources Center at UT
Austin for
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VCH Verlag GmbH & Co. KGaA, Weinheim

220. www.advancedscience.com
Adv. Sci. 2018, 5, 1700625
animal housing and care. This work was funded by National
Institute
of Neurological Disorders and Stroke through R01NS102917
(C.X.) and
R21NS102964 (L.L.), by the UT BRAIN Seed grant award
#365459 (L.L.),
by the Welch Foundation Research grant #F-1941-20170325
(C.X.), and
by DOD CDMRP under award no. W81XWH-16-1-0580 (C.X.).
Conflict of Interest
The authors declare no conflict of interest.
Keywords
electron-beam lithography, flexible neural electrodes, high-
density
intracortical recording, in vivo extracellular recording,
nanofabrication
Received: September 22, 2017
Revised: December 4, 2017

221. Published online: March 10, 2018
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[54] A. C. F. Hoole, M. E. Welland, A. N. Broers, Semicond.
Sci. Technol.
1997, 12, 1166.
[55] A. K. Nallani, S. W. Park, J. B. Lee, Proc. SPIE 2003,
5116, 414.
COMMUNICATION

231. www.advmat.de
1706520 (1 of 7) © 2018 The Authors. Published by WILEY-
VCH Verlag GmbH & Co. KGaA, Weinheim
High-Density Stretchable Electrode Grids for Chronic
Neural Recording
Klas Tybrandt,* Dion Khodagholy,* Bernd Dielacher, Flurin
Stauffer, Aline F. Renz,
György Buzsáki, and János Vörös
Dr. K. Tybrandt, Dr. B. Dielacher, Dr. F. Stauffer, A. F. Renz,
Prof. J. Vörös
Institute for Biomedical Engineering
ETH Zurich
8092 Zurich, Switzerland
E-mail: [email protected]
Dr. K. Tybrandt
Laboratory of Organic Electronics
Department of Science and Technology
Linköping University
601 74 Norrköping, Sweden
Dr. D. Khodagholy
Department of Electrical Engineering
Columbia University

232. New York, NY 10027, USA
E-mail: [email protected]
Dr. D. Khodagholy, Prof. G. Buzsáki
NYU Neuroscience Institute
School of Medicine
New York University
New York, NY 10016, USA
DOI: 10.1002/adma.201706520
Electrical stimulation has successfully
been applied to treat neurological disor-
ders like Parkinson’s disease, epilepsy,
hearing loss, and chronic pain. One
reason for this success is that electrical
stimulation can compensate for scar
tissue encapsulation of electrodes by
increasing the stimulation current and
thereby continuing to reach the stimula-
tion threshold.[1] However, current and
emerging therapies would benefit from
the use of closed feedback systems, in
which recorded signals are used to con-
trol the stimulation for optimal effect.[2,3]
This approach requires the establishment

233. of a well-integrated, long-term stable, elec-
tronic–neural interface to pick up weak
neural signals. One major factor which
restricts the long-term stability of neural
interfaces is the huge mechanical mis-
match between electronics and neural
tissue.[4] Even plastic substrates exhibit
elastic moduli in the GPa range, while neural tissue is vis-
coelastic with elastic moduli in the kPa range.[5] As neural
tissue constantly endures bodily motions and deformations,
stiff implants will cause mechanical wear at the interface,
leading to tissue responses with deteriorating performance
of the implant.[4,6–8] To avoid this effect, neural implants need
to be engineered out of materials that match the mechanical
properties of the interfaced tissue as closely as possible.[9–11]
Flexible electrode grids[12,13] and penetrating probes[14,15]
have
been developed to improve the electrode–tissue interface. In
recent years, further steps in this direction have been taken
by the development of soft and stretchable electrically con-
ductive composites, typically comprising a conductive filler
embedded in an elastomer matrix.[16–21] Biomedical implants
have a strict set of material requirements, including long-term
stability, nontoxicity, and biocompatibility. This disqualifies

234. most reported composites, as silver is unstable and toxic,[1,10]
while carbon nanotube based composites require detergents/
ionic liquids that may leak out into the tissue.[18] Microcracked
gold is to date the premier example of an implantable stretch-
able conductor,[22,23] and it has been used to fabricate soft and
stretchable electrode arrays for stimulation and recording.[11]
Although very useful, microcracked gold has high sheet resist-
ance and reported fabrication protocols have not allowed for
the far-reaching miniaturization necessary for high-resolution
neural recording. To tackle these limitations, here we report
on the development of a novel inert high-performance
Electrical interfacing with neural tissue is key to advancing
diagnosis and
therapies for neurological disorders, as well as providing
detailed information
about neural signals. A challenge for creating long-term stable
interfaces
between electronics and neural tissue is the huge mechanical
mismatch
between the systems. So far, materials and fabrication processes
have restricted
the development of soft electrode grids able to combine high
performance,
long-term stability, and high electrode density, aspects all

235. essential for neural
interfacing. Here, this challenge is addressed by developing a
soft, high-density,
stretchable electrode grid based on an inert, high-performance
composite
material comprising gold-coated titanium dioxide nanowires
embedded in a
silicone matrix. The developed grid can resolve high
spatiotemporal neural
signals from the surface of the cortex in freely moving rats with
stable neural
recording quality and preserved electrode signal coherence
during 3 months of
implantation. Due to its flexible and stretchable nature, it is
possible to minimize
the size of the craniotomy required for placement, further
reducing the level
of invasiveness. The material and device technology presented
herein have
potential for a wide range of emerging biomedical applications.
Soft Neural Interfaces
© 2018 The Authors. Published by WILEY-VCH Verlag GmbH
& Co. KGaA,

236. Weinheim. This is an open access article under the terms of the
Creative
Commons Attribution-NonCommercial License, which permits
use,
distribution and reproduction in any medium, provided the
original
work is properly cited and is not used for commercial purposes.
The copyright line for this article was changed on 7 Mar 2018
after
original online publication.
The ORCID identification number(s) for the author(s) of this
article
can be found under https://doi.org/10.1002/adma.201706520.
Adv. Mater. 2018, 30, 1706520
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh

237. Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
www.advmat.dewww.advancedsciencenews.com
1706520 (2 of 7) © 2018 The Authors. Published by WILEY-
VCH Verlag GmbH & Co. KGaA, Weinheim
stretchable conductor and the corresponding device technology
for high-density, stretchable electrode grids (SEGs) for neural
recording. The composite is based on gold-coated titanium
dioxide nano wires (Au-TiO2NWs), and has good conductivity,
electro mechanical properties, and long-term stability. Based
on this material, we present a miniaturized soft SEG device
technology, which exhibits the highest electrode density of any

238. SEG to date. This feature allows us to chronically implant the
device onto the somatosensory cortex in freely moving rats and
record high spatiotemporal neural signals during 3 months of
implantation. The benefits of the soft and stretchable device
technology are also manifested as conformal contact to the
tissue and self-expansion after insertion through a smaller cra-
nial window than the probe geometry.
To avoid any potentially toxic seed particles or capping
agents, we utilized titanium dioxide nanowires (TiO2NWs) as
the starting material (Figure S1, Supporting Information). The
TiO2NWs were dispersed in a mixture of the biocompatible cap-
ping agent poly(vinylpyrrolidone) (PVP) and the mild reducing
agent hydroxylamine (HDA), followed by addition of chloro-
auric acid (HAuCl4) solution to the mixture under stirring via
a syringe pump (Figure 1a). The optimized procedure resulted
in a conformal coating of gold on the TiO2NWs (Table S1, Sup-
porting Information). To stabilize the nanowire dispersion,
hydrochloric acid was subsequently added, which also resulted
in a color change from grey–brown to orange–red over a few
days
(Figure 1b). The Au-TiO2NWs sedimented within an hour, but
could be redispersed even after half a year without any notice-
able aggregation. The growth process of the gold was studied
by extracting samples at various stages of the coating process.

239. When 1/10 of the gold salt had been added, the TiO2NWs
were partially covered by a thin layer of gold (Figure 1c).
Once all gold salt had been reduced, the TiO2NWs were fully
covered by a rough layer of gold (Figure 1d,e). A week after
adding the hydrochloric acid, the roughness of the gold coating
had decreased significantly (Figure 1e). For electrical charac-
terization, samples of 0.5 mm width and 20 mm length were
prepared by masked vacuum filtration.[24] First, the nanowire
dispersion was filtered through, followed by a rinsing step
of phosphate-buffered saline (PBS) filtration and drying on a
hotplate. Samples prepared from fresh dispersions showed
decreasing sheet resistance over time (Figure 1f). A similar
trend emerged for samples prepared from stored dispersions
(Figure 1f). Stretchable samples were fabricated by transferring
the Au-TiO2NW patterns from the filter membranes to semi-
cured poly(dimethylsiloxane) (PDMS) substrates, followed by
spin coating of a top PDMS layer to encapsulate the Au-
TiO2NW
tracks. The initial sheet resistance of the ≈3 µm thick tracks
was 0.63 ± 0.03 Ω □−1, which translates into a conductivity of
≈16000 S cm−1. When stretched, the sheet resistance increased
to ≈3 Ω □−1 for 50% strain and ≈7 Ω □−1 for 100% strain.
Some
samples started to lose electrical contact above 100% strain, and

240. the samples broke mechanically between 150% and 200% strain.
The composite showed excellent strain cycling stability when
cycled to 20%, 50%, and 100% strain 1000 times (Figure 1h).
The addition of Au-TiO2NWs only caused a minor (12%) stiff-
ening of the samples at 50% strain (Figure S2, Supporting
Information).
To fabricate high-density SEGs, we employed a recently
developed method based on photolithographic masking of filter
Adv. Mater. 2018, 30, 1706520
Figure 1. Stretchable Au-TiO2NW-PDMS composite. a) The
TiO2NWs are coated with gold by reducing HAuCl4 salt with
HDA in the presence of a
capping agent (PVP). b) The addition of HCl stabilizes the
dispersion and promotes a color change from brown–grey to
orange–red. c–e) SEM images
of the Au-TiO2NWs at different stages during the coating
process. c) A partial gold coating is produced when 1/10 of the
gold salt solution has been
added. d) At the end of the reaction, a continuous gold coating
has been produced. e) The initial gold coating is rough (left),
but smoothens out after
one week (right). f ) The sheet resistance RS of films made from

241. freshly produced Au-TiO2NWs improves over time (◾). The
conductivity of the NWs
stored in the dispersion improves over time as well (⚫). g)
Stretchable conductors are produced by embedding the NWs in
PDMS. The sheet resistance
remains below 10 Ω □−1 up to 100% strain. h) The conductors
show excellent stability for 1000 strain cycles at 20%, 50%, and
100% strain. Scale bars:
c,d) 500 nm, e) 200 nm.
⚫
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh

242. Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
Zeyad Alsheikh
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1706520 (3 of 7) © 2018 The Authors. Published by WILEY-
VCH Verlag GmbH & Co. KGaA, Weinheim
membranes.[25] The method can produce arbitrary nanowire
patterns down to 20 µm sized features and yields high
performance, narrow, stretchable conductor lines. The
Au-TiO2NW dispersion was filtered through the masked mem-
brane, cleaned by filtering of PBS buffer, dried, and then trans-
ferred to a semi-cured PDMS substrate (≈75 µm thick). The
membrane was wetted from the backside and peeled off, leaving

243. the transferred pattern on the PDMS substrate. Next, a thin top
layer of PDMS (≈6 µm) was spin coated onto the substrate and
cured. Electrodes and contacts were created by patterning of a
dry film resist followed by dry etching until the Au-TiO2NWs
were exposed. Finally, the SEG was clamped on a custom-made
connector and a thin layer of platinum was electroplated onto
the exposed Au-TiO2NWs to form the electrodes (Figure S3,
Supporting Information). The resulting soft SEG is shown in
Figure 2a and was ≈80 µm thick and 3.2 mm wide at the tip (see
Figure S4 in the Supporting Information for full fabrication
scheme). The SEG had 8 × 4 electrodes with 200 µm electrode
spacing, enabled by the 30 µm wide conductor lines (Figure S5,
Supporting Information). The softness and stretchability
allowed the SEG to conform to curvilinear shapes (Figure 2b).
The electrodes were ≈50 × 50 µm in size (Figure 2c), and the
electrode openings were ≈7 µm deep (Figure 2d). SEM images
revealed the nanostructure of the electrodes, in which the plat-
inum coated nanowires were partially embedded in the PDMS
underneath them (Figure 2e, Figure S6, Supporting Informa-
tion). The platinum coating decreased the impedance of the
electrodes by a factor of 5 (Figure 2f,g), resulting in an areal
capacitance of ≈2.7 mC cm−2. The plating process also
improved
the electrode stability during electrical stimulation, with no

244. degradation in performance after 106 bipolar pulses (1 V,
200 µs) of the physiologically relevant current range of 100 µA
(Figure S7, Supporting Information). When the electrodes were
stretched to 30% strain (Figure 2h), the impedance decreased
slightly in accordance with the increase in electrode area
(Figure 2i). A batch of 16 SEGs was fabricated for this study,
of which 7 devices were fully functional. The defective SEGs
had a few breaks on the narrow conductor lines and were there-
fore discarded.
In vivo evaluation and characterization of the SEGs were
carried out in rats by implanting probes on the somatosensory
cortex (Figure 3a) to record somatosensory evoked potentials
(SSEPs).[26] The recordings were performed both acutely in the
intraoperative setting, as well as chronically in freely moving
rats
(Figure 3c), to further characterize the spatiotemporal
resolution
of neural signals captured by the soft and stretchable probes.
Owing to the softness and stretchability of the SEG, it could be
folded on itself and inserted through an ≈30% narrower crani-
otomy window than its original size. Once inserted, the grid
self-
expanded onto the pial cortical surface (Figure 3b). For chronic
in vivo recording, the grid was covered with a piece of bioab-

245. sorbable gelatin compressed sponge (Gelfoam) and the crani-
otomy was sealed with silicone elastomer. Physiological record-
ings were performed while rats engaged in normal behavior
in their home cage, and intraoperative recordings were carried
out under isoflurane anesthesia. Neural signals were amplified,
multiplexed and digitized using a head-stage mounted directly
on the grid to minimize environmental noise contamination.
SSEPs were performed by insertion of two 36 AWG gold
plated wires into the hind limb of the rat contralateral to the
Adv. Mater. 2018, 30, 1706520
Figure 2. Soft and stretchable electrode grids. a) The electrode
grid is made of Au-TiO2NW conductors embedded in PDMS
and comprises 32 electrodes
with 200 µm pitch. b) The stretchability allows the SEG to
establish conformal contact around curved surfaces. c) An
opening in the PDMS layer is etched
to form the 50 µm large electrodes. A thin layer of platinum is
electroplated onto the exposed Au-TiO2NWs to improve
electrode performance. d) Optical
surface profilometry reveals that the electrode openings are ≈7
µm deep. e) The SEM image of the electrode shows how the Pt-
coated Au-TiO2NWs are

246. partially embedded in the PDMS. f ) The impedance of the
electrode before (—) and after (—) electroplating of platinum.
g) Measured impedance at
1 kHz of the electrodes of an SEG before (♦) and after (⚫) Pt
coating. h) Microscopy images of the electrode at 0% and 30%
strain. i) The impedance
of an electrode at 0% (—) and 30% (—) strain. Scale bars: a) 1
mm, b) 500 µm, c) 50 µm, e) 20 µm and 1 µm, h) 50 µm.
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1706520 (4 of 7) © 2018 The Authors. Published by WILEY-
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craniotomy location under isoflurane anesthesia. Current
pulses with 1 ms duration were titrated until movement of the
digits was visually observable. Pulses were repeated every 3 s
for 10–15 min. Figure 3d illustrates the unfiltered SSEP time
traces recorded by the SEG with clear spatial patterning of the
evoked potential. Furthermore, such a spatial gradient is con-
sistent with the anatomical placement of the probe, illustrated
by the heat map on the dorsal view of the rat skull. Importantly,
throughout the recording, the majority of the electrodes (>85%)

247. were functional and exhibited similar electrical impedance,
highlighting the reliability of the fabrication process of the
SEGs.
To further explore the capacity of the soft SEGs, the devices
and the data generated by them should be validated for sys-
tems neuroscience contexts and experiments. Furthermore, for
neural recordings to be maximally useful, they should be stable
over time. The intraoperative recordings demonstrated that soft
SEGs can generate high signal to noise ratio neural data. There-
fore, we moved forward to record high spatiotemporal resolu-
tion signals in chronic preparations over an extended period of
time. Figure 4a,b shows a sleep epoch acquired by the SEG that
consists of sleep spindle (σ) and gamma (γ) oscillations during
nonrapid eye movement (NREM) sleep epochs, and theta (θ)
and gamma oscillations during rapid eye movement (REM)
sleep epochs. The ability to capture these oscillations in chronic
experiments highlights the stability of the recording, and
health of the surrounding neural tissue. At a finer timescale,
these recordings revealed several high-frequency oscillations
with defined spatial extent localized on the SEG (Figure 4c).
Due to the high-density and large spatial coverage of the elec-
trodes of the SEG, we could investigate the spatial extent of
these oscillations by determining the coherence over distance

248. for various frequency bands (1 to 90 Hz) in the chronically
implanted SEG (Figure 4d). As predicted by theoretical models
of the spatial scale of the local field potential (LFP),[27,28]
coher-
ence was high at low frequencies and decreased for higher
frequencies, highlighting the physiological nature of these
oscillations. Moreover, these analyses suggested that LFP pat-
terns can be differentiated at sub-millimeter distances and that
high spatiotemporal resolution sampling of neural activity can
provide information about physiological cortical processes. The
recording quality was stable over time, as the average power
of the recorded signals was similar after 1, 2, and 3 months of
implantation (Figure 4e). After 3 months, 28 electrodes showed
excellent signal quality, 3 had weaker signals, and only one
elec-
trode was dead (Figure 4f, Figure S8 in the Supporting Infor-
mation). As the failing electrodes were not adjacent to each
other, but connected next to each other on one side of the con-
nector (Figure S4, Supporting Information), it is likely that the
failure is related to the grid-connector contact rather than the
stretchable conductor or the electrode–tissue interface.
The coating of the TiO2NWs starts by the formation of a thin,
partially covering gold layer. As the pH of the dispersion ranges
from 6 to 7 during the synthesis, the titanium dioxide is neu-

249. tral[29] while the gold (nanoparticles) are negatively
charged.[30]
As PVP further reduces any surface charge, the adsorption of
the initial gold layer is thus probably not driven by electrostatic
forces, but rather by van der Waal’s attraction. This hypothesis
suggests that other template materials, like carbon nanotubes
or fibers, could be used in a similar fashion, although little
gain in performance would be expected due to the superior
conductivity of gold. The coating process is effective, as no free
gold (nano)particles can be seen in the SEM images or in the
filtrate. This is probably due to the chosen reducing agent’s
Adv. Mater. 2018, 30, 1706520
Figure 3. Implantation and in vivo neural recording of soft
SEGs. a) Intraoperative microphotograph of soft SEG showing
the probe in a craniotomy
lying on the surface of the brain. Note the hydrophobic nature
of the device demonstrated by a drop of water on the ribbon
area. b) Zoomed-in
microscopy image showing the ability of the probe to be
inserted in smaller craniotomy than the probe geometry. c)
Freely moving rat with chronically
implanted SEG. d) Intraoperative recording of SSEPs using the
soft SEG. Top: raw LFP time traces showing SSEPs generated

250. by hind limb electrical
stimulation. Right: heat map demonstrating the anatomical
localization of SSEPs to the somatosensory cortex. Left: electric
impedance of the SEGs at
1 kHz, showing a uniform impedance range across devices.
Scale bars: a,b) 1 mm, c) 10 mm, d) 100 ms, 500 µV.
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1706520 (5 of 7) © 2018 The Authors. Published by WILEY-
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surface-catalyzed reduction of Au3+, which prevents continuous
seeding of new gold nanoparticles.[31] The addition of HCl
to the dispersion induces a gradual color change, which is
caused by smoothening of the initially rough gold surface. The
smoothening improves the electrical conductivity, giving the
Au-TiO2NW-PDMS composite a conductivity of ≈16000 S
cm−1,
which compares well with previously reported stretchable com-
posites.[16,17,20,32] Even better, the electromechanical
character-
istics remain stable after one thousand strain cycles of 100%

251. strain, which is important for a material considered for long-
term use. A possible explanation for this advantageous behavior
is the small size of the cracks in the composite under strain
(Figure S9, Supporting Information) in comparison to silver
nanowire-based composites.[24] This observation might in turn
be related to the relatively low degree of stiffening caused by
the Au-TiO2NWs.
The fact that Au-TiO2NWs are long-term stable and can be
dispersed in water without ultrasonication makes the mate-
rial convenient to use. Further, the developed filtration method
exhibits certain advantages for biomedical applications: (i) the
material consumption is low which allows for the use of expen-
sive noble metals; (ii) the nanowires can easily be cleaned in
the process, thereby removing potentially harmful chemicals;
(iii) the method yields high-quality micro-patterns that enable
high electrode density; (iv) PDMS or other medical grade
silicones can be used in the process; (v) the patterning process
does not expose the sensitive PDMS substrate to any aggres-
sive organic solvents or potentially harmful substances. The
impedance of the microelectrodes is around 10 kΩ at 1 kHz in
PBS, which is low enough for high-quality recordings. When
fitting an equivalent circuit to the impedance data (Figure S10,
Supporting Information), a constant phase element is needed.

252. This indicates that the electrodes are porous, which is in line
with the high areal capacitance (2.7 mC cm−2) and the SEM
images. Our SEG outperforms previously reported devices in
terms of electromechanical properties, electrode density, and
electrode performance (Table 1), all key features for high-
quality
neural recording. Also, our SEG has demonstrated stable
neural recordings for the longest period of any implanted SEG.
Although nanomaterials are great for electrode performance,
one need to consider the risk of nanotoxicity, often mediated
through the creation of reactive oxygen species (ROS).[33]
TiO2
nanoparticles have been found to cause relatively low levels of
ROS induced stress in cells.[34,35] However, this should not be
an issue in this work as the electrodes and Au-TiO2NWs can
be fabricated without any visible exposure of TiO2 (Figure S6,
Supporting Information). Overall, these features make the
developed technology a premier candidate for a wide range of
biomedical applications.
The soft and stretchable nature of the SEG allowed us to per-
form craniotomies that were smaller than the probe’s geometry,
reducing the risk of surgical complications and cerebral
swelling.
Moreover, the hydrophobic surface of the probe provided an

253. efficient contact with the brain and minimized the cerebro-
spinal fluid shunting effect across electrodes. This was high-
lighted by localization of the somatosensory potentials during
acute recording and high-frequency oscillations during chronic
recordings. The fabrication process produced high-density grids
with large spatial coverage that allowed determination of the
spatial extent of brain oscillations for various frequency bands
Adv. Mater. 2018, 30, 1706520
Figure 4. In vivo chronic recording and spatial distribution of
local field potential. a) Time–frequency spectrogram of LFP
recorded by the soft SEG
during sleep in somatosensory cortex. The spectrogram during
an NREM epoch contains slow oscillations (2–4 Hz) and sleep
spindles (9–16 Hz);
during a REM epoch theta (4–8 Hz) and gamma (30–80 Hz)
oscillations were seen. Colors represent normalized power, with
warmer colors indicating
higher power. b) Raw LFP trace (top) and corresponding
spectrogram (bottom) from 3 month chronically implanted SEG
during an NREM epoch.
c) Raw LFP trace highlighting a spatially patterned gamma
oscillation during an NREM epoch. d) Average coherence
between recording electrodes

254. at each frequency band in an NREM session (— 2–4 Hz), (— 4–
8 Hz), (— 9–16 Hz), (— 30–80 Hz). e) Mean power of recorded
signal after 1 (—),
2 (—), and 3 (—) months of implantation. f ) Number of
functional recording electrodes after 1, 2, and 3 months of
implantation, with high-quality
recording electrodes in blue and low-quality recording
electrodes in light blue. Scale bar: c) 100 ms.
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(1–90 Hz) at sub-millimeter resolution. In clinical practice,
grid arrays can provide a 2D localization of epileptic foci. How-
ever, obtaining such information can be limited by the size
of the cranial window required to insert such an array. The
ability of the SEGs to be inserted through smaller cranial win-
dows would potentially enable acquisition of such spatial data
while reducing the size of the cranial window. Altogether, we
believe that the presented technology constitutes an attractive
platform for the continued development of mechanically com-

255. pliant neuronal probes for long-term recording. Specifically, the
adaptation of the technology to penetrating probes should be
especially rewarding due to the softness and deformability of
the probes.
Experimental Section
Au-TiO2NWs: TiO2 nanowires (ACSMaterial, TiO2NW-A,
length
≈10 µm, 0.15 mg), hydroxylamine (Sigma-Aldrich, 50%, 61
µL), and
poly(vinylpyrrolidone) (Sigma-Aldrich, MW 50k, 0.33 g) were
mixed
with deionized (DI) water to a final volume of 9.5 mL. Gold(III)
chloride
solution (Sigma-Aldrich, Au 17 wt%, 61 µL) was diluted in DI
water to
a final volume of 10 mL. The gold solution was added with a
syringe
pump into the TiO2NW solution under stirring (1 mL at 1.4 mL
min
−1,
then 9 mL at 0.7 mL min−1). Hydrochloric acid (Sigma-Aldrich,
37%,

256. 0.5 mL) was added 5 min after completion. The dispersion was
put through a 20 µm stainless steel sieve to remove eventual
large
aggregates. The dispersion was stored for at least 1 d before
use. SEM
images were taken with a Zeiss Leo 1530.
Electromechanical Characterization: Au-TiO2NW tracks (20
mm
length, 0.5 mm width) with contact pads were patterned by wax
assisted
vacuum filtration[24] (4 mL Au-TiO2NW solution, 200 mm
2 open filter
area) and transferred to a semi-cured PDMS sample (Sylgard
184,
Dow Corning, 1:10, 1 krpm spin speed, 30 s, 6 min 70 °C
curing). A
diluted PDMS solution (PDMS:heptane 1:20) was spin coated (6
krpm
60 s) to reinforce the contact pads and cured (80 °C, 20 min). A
25 µm
poly(ethylene naphthalate) foil was used to mask the contact
pads and
a layer of PDMS was spin-coated on top (2 krpm 30 s). The foil

257. was
removed and the samples were cured (80 °C, 16 h). The samples
were
clamped in a tensile testing machine (DO-FB0.5TS,
Zwick/Roell) with
copper tape as contacts. A digital multimeter (Agilent 34401A)
was used
to measure the resistance at 4 Hz. The strain rate was 0.5 mm
s−1 for
the maximum strain tests and 2 mm s−1 for the cycling tests.
SEG Fabrication and Characterization: Hydrophilic PVDF
membranes
(0.22 µm pore size, Millipore) were patterned with ma-N 490
photoresist (micro resist technology GmbH) according to
previously
published protocol.[25] Au-TiO2NW dispersion (1.8 mL, 92 mm
2 open
filter area) followed by PBS buffer (10 mL) was vacuum filtered
through
the patterned membrane. PDMS was spin-coated (Sylgard 184, 1
krpm
spin speed, 30 s) onto a silanized 2 inch glass wafer and semi-
cured

258. (70 °C, 6 min). The dried membrane was put in contact with the
PDMS
under pressure (80 °C, 10 min) and peeled off after being
soaked in DI
water. A diluted PDMS solution (PDMS:heptane 1:20) was spin
coated
(6 krpm 60 s) and cured (80 °C, 20 min), followed by another
spin-coated
PDMS layer (6 krpm, 120 s) and curing (80 °C, 16 h). After 10
min UV
ozone treatment, a poly(vinyl alcohol) (PVA) layer was spin-
coated on
top (10%, 3 krpm, 60 s). A dry film resist layer was laminated
on top
(LP Dry Film Photopolymer), exposed, and developed. The
substrate was
dry etched (RIE, 250 W, 100 mTorr, O2/CF4, 400 s) and the
PVA and dry
resist stripped in boiling DI water. The SEG was cut out with a
scalpel
and clamped on a custom-made printed circuit board connector.
Finally,
a thin layer of platinum was electroplated onto the electrodes
(Sigma-

259. Aldrich, chloroplatinic acid solution 0.8 wt% in DI water,
pulsed −1.5 V,
20 Hz, 10% duty cycle).
Animal Surgical Procedure: All animal experiments were
approved by
the Institutional Animal Care and Use Committee at New York
University
Langone Medical Center. Three male rats (200–350 g, 8–15
weeks of age)
were used for SEG acute and chronic implantations. Rats were
kept on a
regular 12–12 h light–dark cycle and housed in pairs before
implantation,
but separated afterward. No prior experimentation had been
performed
on these rats. The animals were initially anaesthetized with 2%
isoflurane
and maintained under anesthesia with 0.75–1% isoflurane
during the
surgery. A 2 × 3 mm2 craniotomy over somatosensory cortex
(AP -3.0 ML
3.5) was performed. This was followed by dura-mater removal,
resulting
in an effective 2 × 2.5 mm2 exposed brain surface. The SEG

260. was folded
and inserted into the craniotomy and self-expanded onto the pial
surface
of the brain. After all surgical procedures, the craniotomies
were covered
with gel-foam and sealed using a silicone elastomer. Once the
rats
had recovered for 5 d postsurgical procedure, somatosensory
evoked
potentials were performed by insertion of two 36 AWG gold
plated wires
into the hind limb of the rat contralateral to the cranial window
under
isoflurane anesthesia. Current pulses with 1 ms duration were
titrated
until movement of the digits was visually observable. Pulses
were
repeated every 3 s for 10–15 min.
Supporting Information
Supporting Information is available from the Wiley Online
Library or
from the author.
Acknowledgements

261. The authors acknowledge M. Lanz and S. Wheeler for their
valuable
technical support, and the support of the ETH ScopeM facility
for
performing scanning electron microscopy experiments. The
research
was financed by the Swedish Research Council (637-2013-
7301), the
Swedish Government Strategic Research Area in Materials
Science on
Functional Materials at Linköping University (Faculty Grant
SFO Mat
LiU No 2009 00971), the Swiss Nanotera SpineRepair project,
and ETH
Adv. Mater. 2018, 30, 1706520
Table 1. Comparison between the SEG in this work and
previously reported SEGs.
Conductor RS
[Ω]
Max strain
[%]

262. Electrode
material
Electrode
count
Electrode densityc)
[mm−2]
Electrode size
[103 µm2]
|Z|d) at
1 kHz [kΩ]
|Z|·Ae) at
1 kHz [kΩ cm2]
Implantation
period [weeks]
Ref.
Au-TiO2-NWs 0.6 100 Pt plated NWs 32 38 3 10 0.3 13 This

263. work
PPy/PCTC 6 23 PPy/PCTC 4 0.2 800 10 80 Acute [36]
PPy/PTSA 8 9b) PPy/PTSA 4 0.9 200 30 60 Acute [37]
Au film 35 nm 9a) >45 Pt-PDMS 9 3 70 3 2 6 [11]
Au film 50–150 nm - ≈10 Au 11 3 3 200 6 in vitro [38]
Au film 38 nm – ≈10 Pt 28 11 10 100 10 in vitro [39]
a)RS not given, adopted from ref. [22];
b)In prestrained device 110%; c)Area calculated from rectangle
enclosing the electrodes; d)|Z| is impedance; e)Areal
impedance.
www.advmat.dewww.advancedsciencenews.com
1706520 (7 of 7) © 2018 The Authors. Published by WILEY-
VCH Verlag GmbH & Co. KGaA, Weinheim
Zurich and NIH grants UO1NS099705 and U01NS090583. D.K.

264. was
supported through the Simons Foundation (junior fellow). K.T.
designed,
and K.T., B.D., F.S., and A.F.R. performed the material and
device
related experiments. D.K. designed, performed, and analyzed all
animal
experiments. K.T., D.K., G.B, and J.V. conceived the project.
K.T. and D.K.
wrote the first draft of the manuscript and all authors
contributed to the
finalization of the paper.
Conflict of Interest
The authors declare no conflict of interest.
Keywords
nanowires, neural electrodes, neural interfaces, soft electronics,
stretchable electronics
Received: November 8, 2017
Revised: January 19, 2018
Published online: February 28, 2018

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