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Chip and System Design forOn-Body Wireless Sensing    Prof. Brian Otis    University of Washington    Electrical Engineeri...
Focus of this talk: system building forwireless sensors   COTS vs. custom chips?   Wireless power vs. battery power?   ...
Traditional miniaturized system design                                     MCU     FRAM                                   ...
U.W. Encounternet                                                     • Sub-gram digital                                  ...
Typical power profile   Power           t                      Standby                      <10μW     Tx (-10dBm)         ...
Problems  Powertoo high for continual streaming  applications (dominated by radio)  Size      too large for emerging bio...
Wireless communication modalities1.   Miniaturized battery-powered systems2.   Miniaturized wirelessly-powered systems    ...
Wireless communication modalities1.   Miniaturized battery-powered systems2.   Miniaturized wirelessly-powered systems    ...
Chip architecture    8
9
   350 mg   4 channel   18 ksps A/D   200 kbps Tx   22 hour                  10
Human ECG experiment                       11
Human ECG            Two hours of ECG data            (>20Mb) streamed to a            laptop and reconstructed           ...
Wireless communication modalities1.   Miniaturized battery powered systems2.   Miniaturized wirelessly-powered systems    ...
Case study: an RFID-like system        Reader                   Power                    Tag                              ...
Architecture (Yeager et al., JSSC2010)                     Bandgap             Diode Ref.      50nA BiasOptional C, amps  ...
Battery-free wireless sensor   -12 dBm sensitivity       3m range   RFID Gen2 Compatible       Talks to COTS Readers ...
Hawkmoth Experiment                                      17     Prof. Tom Daniel, U.W. Biology
Power source comparison1.   Miniaturized battery powered systems      Much longer wireless range      Relaxed antenna sp...
Wireless Neural Interfaces Potential to revolutionize neuroscience research;medicine: epilepsy, Parkinson’s, hearing loss...
Are we there yet? Adequate noise/BW for EEG, ECoG, single-unit Not sufficient for cuff electrode recordings   20
Signals in the Noise?   Noise specifications often    based on “background noise”       Aggregate neural activity of    ...
Noise: How good is good enough?                                              4kTR   Fundamental limit:    Electrode resis...
How do we get there - More Power? Power & noise typically trade off Tissue necrosis a risk at P > 80 mW/cm2 Low-frequen...
How - Improved Amp Topologies?   Single-ended amplifier,    complementary input     Current ➘ by 4x     Poor power supp...
So what?1. We can make extremely small battery-   powered and battery-free wireless systems2. Wireless connectivity is typ...
Questions?         26
Access point USB/PC interface Analog out for audio spike observation LEDs indicate signal strength Indoor range ~24 me...
Data (and metadata)                      28
Mouse barrel cortex recording(collaboration with the Allen Institute for Brain Sciences)
Session statistics   Long term in vivo recording performed overnight   11.5 hours of data collected   Over 750 million ...
How do we get there – Devices?   BJTs     Base current, input      resistance can      (maybe) be cancelled     Relecin...
Technology Impact                    Cost                    Reliability                    Security                    Us...
How - Chopper modulation? Moves signal to higher  frequency to avoid 1/f noise Modulation converts Cin to resistance    ...
Let’s build a system                       34
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7.2 – Chip and system design for on-body wireless sensing

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Thursday, October 25, 2012
Technical Session #7

Brian Otis (Department of Electrical Engineering, University of Washington, US)

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  • Area = 2mm x 2mm ?
  • Recording life span &lt;&lt; 1 year
  • This is too cluttered and needs clean-up, but I would like to be able to refer to all three figures simultaneously
  • BJT: Assume 1uA, R_elec = 1k
  • Hugo’s case study: high tech implantables. Very expensive. No observability into data! 3M pacemakers0.22M cochlear implants0.08M deep brain stimulators
  • The tradeoff between Rin and noise would be OK. I.e. a trade of 10x noise improvement for a few dB attenuation would be a net win.But, as Rin approaches Relec (and Rsig != Rref) we pick up the CM EMG signal, which may be orders of magnitude larger than the neural signal.
  • Transcript of "7.2 – Chip and system design for on-body wireless sensing"

    1. 1. Chip and System Design forOn-Body Wireless Sensing Prof. Brian Otis University of Washington Electrical Engineering Seattle, WA Prof. Jeremy Holleman University of Tennessee Electrical Engineering & Computer Science Knoxville, TN
    2. 2. Focus of this talk: system building forwireless sensors COTS vs. custom chips? Wireless power vs. battery power? Theoretical & practical design constraints A few design examples 1
    3. 3. Traditional miniaturized system design MCU FRAM MSP430F5528 1Mb 32kHz Watch CrystalExternalinterface:power, SBW, SPI,I2C, GPIO, ADC RF MatchingRF Digital NetworkTransceiverCC1101 Antenna connector RF Crystal 2 15 mm
    4. 4. U.W. Encounternet • Sub-gram digital peer-to-peer radios • On-board storage of “encounter logs”, periodically upload to basestations • Hundreds deployed in the field • >10 years and millions of encounters logged • >1k on order from researchersFemale Manakin, Costa Rica, 2011, Mennill et al.
    5. 5. Typical power profile Power t Standby <10μW Tx (-10dBm) Rx 20mW 30mW 4
    6. 6. Problems  Powertoo high for continual streaming applications (dominated by radio)  Size too large for emerging biomedical sensing paradigms 5
    7. 7. Wireless communication modalities1. Miniaturized battery-powered systems2. Miniaturized wirelessly-powered systems 6
    8. 8. Wireless communication modalities1. Miniaturized battery-powered systems2. Miniaturized wirelessly-powered systems 7
    9. 9. Chip architecture 8
    10. 10. 9
    11. 11.  350 mg 4 channel 18 ksps A/D 200 kbps Tx 22 hour 10
    12. 12. Human ECG experiment 11
    13. 13. Human ECG Two hours of ECG data (>20Mb) streamed to a laptop and reconstructed 12
    14. 14. Wireless communication modalities1. Miniaturized battery powered systems2. Miniaturized wirelessly-powered systems 13
    15. 15. Case study: an RFID-like system Reader Power Tag Data Biosignal Commercial UHF Battery-Free Input RFID Reader Sensor Tag UHF Power Transfer  FCC Limit Transmitter (+30dBm)  3m range ~30μW RF power available after modulation losses RFID provides nearly free uplink data transmission  Tag modulates reflection coefficient 14 RF Power signal can also serve as frequency reference
    16. 16. Architecture (Yeager et al., JSSC2010) Bandgap Diode Ref. 50nA BiasOptional C, amps 1.8V Reg. 1.2V Reg. 0.7V Reg. Dig 3MHz Clock Core ADC Chopper Gen2 Chopper Sensor Amp 8b ADC Test RX Demod Tag Rectifier Logic Structures TX Mod UID RF FE UID RNG 2.5mm  Good: leverage Gen2 RFID protocol & infrastructure  Bad: Gen2 RFID protocol overhead 15
    17. 17. Battery-free wireless sensor -12 dBm sensitivity  3m range RFID Gen2 Compatible  Talks to COTS Readers Zero trimming 16 On-chip biosignal amps, ADC, etc
    18. 18. Hawkmoth Experiment 17 Prof. Tom Daniel, U.W. Biology
    19. 19. Power source comparison1. Miniaturized battery powered systems  Much longer wireless range  Relaxed antenna specifications  More signal processing capability  Potential for data logging2. Miniaturized wirelessly-powered systems  No maintenance or battery changes  No need for stable clock generation  Ultimate minimization of form-factor & mass 18
    20. 20. Wireless Neural Interfaces Potential to revolutionize neuroscience research;medicine: epilepsy, Parkinson’s, hearing loss, SCI. Noise, power, long-term reliability serious obstacles Modality Bandwidth Amplitude Spatial Invasiveness Resolution Single-Unit .1-7 kHz < 500 μV 0.2 mm Invasive Cuff < 5 kHz < 10 μV - Mod. Inv. LFP < 200 Hz < 5 mV 1 mm Mod. Inv. ECoG .5-200 Hz < 100 μV 5 mm Mod. Inv. EEG < 100 Hz 10-20 μV 30 mm Non-Inv. EMG < 1 kHz < 10 mV 19 Interferers Power Line 50/60 Hz 10’s mV
    21. 21. Are we there yet? Adequate noise/BW for EEG, ECoG, single-unit Not sufficient for cuff electrode recordings 20
    22. 22. Signals in the Noise? Noise specifications often based on “background noise”  Aggregate neural activity of many distant units Signal processing can potentially pull more information from a recording with sufficiently low noise Single-unit signal quality deteriorates dramatically over time  Tissue reaction covers electrode 21
    23. 23. Noise: How good is good enough? 4kTR Fundamental limit: Electrode resistance generates noise  NB: Impedance ≠ Resistance Amp Noise < .46 R (kΩ) en Area (nV/√Hz (mm2) Electrode noise for ) < 10% SNR degradation Rubehn 1.7 5.3 3 2009 TakahashiWant to be here 2007 134 47 .006 Molina- 7.5 11 .01 22 Luna 2007 Existing amps leave information on the table
    24. 24. How do we get there - More Power? Power & noise typically trade off Tissue necrosis a risk at P > 80 mW/cm2 Low-frequency noise (1/f) severe in neural frequencies;  Scales w/ area, not power Current amps approaching practical constraints on transistor area For low-channel-count systems, increased power & area offers modest improvement potential 23
    25. 25. How - Improved Amp Topologies? Single-ended amplifier, complementary input  Current ➘ by 4x  Poor power supply rejection Comp. differential amp  Improved linearity, PSRR Feed-forward Error correction  Measure VDD noise, & compensate 24
    26. 26. So what?1. We can make extremely small battery- powered and battery-free wireless systems2. Wireless connectivity is typically the bottleneck in on-body sensing3. Significant circuit design problems remaining1. Chip designers can have a profound influence on next generation biomedical 25 sensing systems
    27. 27. Questions? 26
    28. 28. Access point USB/PC interface Analog out for audio spike observation LEDs indicate signal strength Indoor range ~24 meters 27
    29. 29. Data (and metadata) 28
    30. 30. Mouse barrel cortex recording(collaboration with the Allen Institute for Brain Sciences)
    31. 31. Session statistics Long term in vivo recording performed overnight 11.5 hours of data collected Over 750 million samples received wirelessly (> 6 Gb of data) 1.4*10-7 % data loss due to dropped packets
    32. 32. How do we get there – Devices? BJTs  Base current, input resistance can (maybe) be cancelled  Relecin,b> 2ICq/β JFETs 31
    33. 33. Technology Impact Cost Reliability Security Usability Standards 32
    34. 34. How - Chopper modulation? Moves signal to higher frequency to avoid 1/f noise Modulation converts Cin to resistance  Rin = fChop×CIn 1/f noise power ∝ 1/Cin Noise reduction requires ➚fChop or ➚Cin  Either way, roughly constant noise/Rin Unless Rin >> Relec CM-DM conversion worsens EMG interference 33
    35. 35. Let’s build a system 34
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