Automated Design of Digital Microfluids Lab-on-Chip
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Automated Design of Digital Microfluids Lab-on-Chip

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Automated Design of Digital ...

Automated Design of Digital
Microfluidics Lab-on-Chip
Krishnendu Chakrabarty
Department of Electrical and Computer Engineering
Duke University
Durham, NC
Connecting Biochemistry to Information Technology
And Electronic Design Automation

Talk delivered at ACM Bangalore Distinguished Speaker Program Feb 2009

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    Automated Design of Digital Microfluids Lab-on-Chip Automated Design of Digital Microfluids Lab-on-Chip Document Transcript

    • Automated Design of Digital Microfluidics Lab-on-Chip Connecting Biochemistry to Information Technology And Electronic Design Automation Krishnendu Chakrabarty Department of Electrical and Computer Engineering Duke University Durham, NC 1 Acknowledgments • Students: Tianhao Zhang, Fei Su, William Hwang, Phil Paik, Tao Xu, Vijay Srinivasan, Yang Zhao • Post-docs, colleagues, and collaborators: Dr. Vamsee Pamula, Dr. Michael Pollock, Prof. Richard Fair, Dr. Jun Zeng (HP Labs), Dr. S. Krishnamoorthy (Baxter) • Duke University’s Microfluidics Research Lab (http://www.ee.duke.edu/research/microfluidics/) • Advanced Liquid Logic (http://www.liquid-logic.com/): Start-up company spun out off Duke University’s microfluidics research project 2 1
    • Motivation for Lab-on-Chip • Clinical diagnostics, e.g., healthcare for premature infants, point-of-care diagnosis of diseases • “Bio-smoke alarm”: environmental monitoring • Massive parallel DNA analysis, automated drug discovery, protein crystallization CLINICAL DIAGNOSTIC Lab-on-a-chip for APPLICATION CLINICAL DIAGNOSTICS Shrink Microfluidic Lab- on-a-Chip 20nl sample Higher throughput, minimal human intervention, smaller sample/reagent consumption, higher sensitivity, increased productivity Conventional Biochemical Analyzer 3 The Futility of Predicting Applications Kroemer’s Lemma of New Technology: The principal applications of any sufficiently new and innovative technology have always been—and will continue to be—applications created by that technology. Herbert Kroemer, Department of Electrical and Computer Engineering, University of California at Santa Barbara Nobel Prize winner for Physics, 2000 4 2
    • Tubes to Chips: Integrated Circuits • Driven by Information Processing needs IBM Power 5 IC (2004) IBM 701 calculator (1952) 5 Tubes to Chips: BioChips • Driven by biomolecular analysis needs BioMark™ Dynamic Arrays Fluidigm Test tube analysis 6 3
    • Why Do We Care? System Driver Beyond 2009: “Medical” Intel Research Day 2007: Biochip prototype demonstrated for point-of-care diagnostics and 2007 lab testing 7 Press Releases and News Items L NA UR O J ET RE ST L AL W E TH 8 4
    • Why is Biochemistry-on-a-Chip Difficult? A C B Mixing A+B Synthesis A A+B B Analysis Reaction Separation 9 Why is Biochemistry-on-a-Chip Difficult? 10 5
    • what’s a biochip? By the way, It’s a miniature disposable for an HTS - High-Throughput Screening - (bio)analytical instrument what does it do? Essentially the same operations you did in high school chemistry class: dispensing, mixing, detecting, discarding,- just a lot cheaper and a lot faster than you did 11 Why do chips have to be small? High-Throughput is why. If you do 106 assays in 10μl format, each time you do a reaction you’ll need 10 liters of reagents. With the typical cost of biological reagents, even Big Pharma can’t afford this. why High-Throughput? By the way, • Because you need a lot of raw data for many applications • Because, with the currently available technology, to produce raw data that would keep a CPU busy for a few minutes ($0.1), you need a Ph.D. scientist and a couple of technicians for a month ($10,000) 12 6
    • Talk Outline • Motivation • Technology Overview – Microarrays – Continuous-flow microfluidics: channel-based lab-on-chip – “Digital” microfluidics: droplet-based lab-on-chip • Overview of Fabrication Method • Design Automation Methods – Synthesis and module placement – Droplet Routing – Pin-Constrained Design – Testing and Reconfiguration • Conclusions 13 Microarrays • DNA (or protein) microarray: piece of glass, plastic or silicon substrate • Pieces of DNA (or antibodies) are affixed on a microscopic array • Affixed DNA (or antibodies) are known as probes Hybridized array • Only implement hybridization reaction ♦ ♦ A A T ♦ G G G G C C DNA G T C G T G C C A Sample C T T C T A T G A T A T A A T T A A Optical Scan A C C G T G A C G substrate substrate Unhybridized array Laser 14 7
    • What are the main types of biochips? Passive (array): all liquid handling functions are performed by the instrument. The disposable is simply a patterned substrate. Active (lab-on-chip, μ-TAS): some active functions are performed by the chip itself. These may include flow control, pumping, separations where necessary, and even detection. 15 Motivation for Microfluidics Automation Test tubes Integration Miniaturization Automation Robotics Integration Miniaturization Automation Microfluidics Integration Miniaturization 16 8
    • Microfluidics • Continuous-flow lab-on-chip: Permanently etched microchannels, micropumps and microvalves • Digital microfluidic lab-on-chip: Manipulation of liquids as discrete droplets Multiplexing (Duke University) Mixing: Static, Diffusion Limited Biosensors: Control Optical: SPR, Fluorescence etc. electronics Electrochemical: Amperometric, (shown) are Potentiometric etc. suitable for Printed circuit board handheld or lab-on-a-chip – benchtop inexpensive and applications readily manufacturable 17 Electrowetting • Novel microfluidic platform invented at Duke University • Droplet actuation is achieved through an effect called electrowetting ⎯ Electrical modulation of the solid-liquid interfacial tension No Potential Applied Potential A droplet on a hydrophobic The droplet’s surface energy surface originally has a increases, which results in a large contact angle. reduced contact angle. The droplet now wets the surface. 18 9
    • What is Digital Microfluidics? • Discretizing the bottom electrode into multiple electrodes, we can achieve lateral droplet movement Note: oil is typically used to fill between the Droplet Transport (Side View) top and bottom plates to prevent evaporation. 19 What is Digital Microfluidics? Transport 25 cm/s flow rates, order of magnitude higher than continuous-flow methods For videos, go to www.ee.duke.edu/research/microfluidics 20 10
    • What is Digital Microfluidics? Splitting/Merging 21 Demonstrations of Digital Microfluidics Droplet Formation Synchronization of many droplets http://www.ee.duke.edu/research/microfluidics 22 11
    • What is Digital Microfluidics? Droplet Formation 8 droplets in 3.6s 23 What is Digital Microfluidics? Mixing 24 12
    • Advantages • No bulky liquid pumps are required – Electrowetting uses microwatts of power – Can be easily battery powered • Standard low-cost fabrication methods can be used – Continuous-flow systems use expensive lithographic techniques to create channels – Digital microfluidic chips are possible using solely PCB processes Droplet Transport on PCB (Isometric View) 25 An Example • Detection of lactate, glutamate and pyruvate has also been demonstrated. • Biochip used for multiplexed in-vitro diagnostics on human physiological fluids Fabricated microfluidic array used for multiplexed biomedical assays 26 13
    • Capabilities • Digital microfluidic lab-on-chip TRANSPORT DISPENSING MIXERS REACTORS DETECTION TRANSPORT DISPENSING MIXERS REACTORS DETECTION Basic microfluidic functions (transport, splitting, merging, and mixing) have already been demonstrated on a 2-D array INTEGRATE Highly reconfigurable system Digital Microfluidic Biochip Protein crystallization chip (under development) 27 Advantages of Digital Microfluidics Digital Microfluidics Other Microfluidic Technologies • Very accurate droplet volumes • Pump fluids through channels – Droplet sizes in the 1 nanoliter to several • Must adapt assays to channel- microliter range; droplet dispensing volume based format variation ~1% • Complex or multiplexed assays • Programmable, software-driven electronic become a plumber’s nightmare control • Off-chip pumps and valves mean – No moving parts, tubes, pumps or valves large, expensive equipment and • More efficient use of samples and reagents low reliability – No liquid is wasted priming channels • Expensive, time consuming, up- • Extremely energy efficient front investments required for most – Nanowatts of power per single step of chip developments actuation • Designs are fixed in the • Development cycles are short, and assays development process can be implemented with software changes • Compatible with live biologic and most other materials •Droplets moved in “virtual channels” defined by electrodes •Programmable electrodes directly control Caliper Technologies’ discrete droplet LabChip operations 28 14
    • Glass Chip Platform Development Top Plate (Optional) (i.e. glass or plastic) Gasket Layer (100 to 600 µm) (proprietary) Hydrophobic Layer (50 nm) (i.e. Teflon dip coated) Insulator Layer (1 to 25 µm) (i.e. parylene) Patterned Metal on Substrate (i.e. chrome on glass via lift-off process) Chip Assembly Top plate is either glued or fixed in place by pressure Contacts are made either through the top or bottom Droplets are either dispensed by hand or formed from on-chip reservoirs 29 PCB Chip Platform Development Fabrication Process Gasket Layer Flash Plating (Dry Soldermask) (Copper) Hydrophobic Layer (Teflon AF) Dielectric (LPI Soldermask) PCB Top Metal Layer (Copper) Bottom Metal Layer (Copper) Via Hole Filling (Non Conductive Epoxy) • PCB Material – Mitsui BN300 – 64 mil • Top Metal Layer (Electrodes) – Cu – 15µm • Bottom Metal Layer (Contacts) – Cu – 15µm • Dielectric – LPI Soldermask – 25 µm • Via Hole Filling – Non-conductive Epoxy • Hydrophobic Layer – Teflon AF – 0.05 to 1.0 µm • Gasket (spacer) – Dry Film Soldermask (Vacrel 8140) – 4 mils (~95µm after processing) 30 15
    • Computer-Aided Design: Vision • Automate labor-intensive tasks, reduce burden on chip users – Map bioassays to a fabricated chip: schedule fluidic operations, determine droplet flow pathways, configure fluidic modules dynamically, etc. – Monitor the chip for defects that require remapping of bioassays • Role of computer-aided design (CAD) tools – Reduce setup time associated with the use of these chips – Allow automatic reconfiguration of a faulty chip and remap the remaining steps of bioassay. – Develop capabilities that mirror compiler and operating system support provided to software programmers – Obviate the need for tedious remapping of assays to the chip by hand for each target application. • Similar to an FPGA? Logic Interconnects 31 CAD = ? cad n. An unprincipled, ungentlemanly person CAD abbr. Computer-Aided Design Better to be in CAD than to be a cad? 32 16
    • The Road Not Taken… I shall be telling this with a sigh Somewhere ages and ages hence: Two roads diverged in a wood, and I- I took the one less traveled by, And that has made all the difference. Robert Frost, The Road Not Taken Agilent’s Protein Nanogen’s NanoChip™ LabChip Microelectronic Array Cartridge i-STAT Biodiagnostic μ-system 33 Similar to Concurrency in a PC! January 28, 2007 Prof. Radu Marculescu Attn: Outstanding Ph.D. Dissertation Award Carnegie Mellon University Department of Electrical and Computer Engineering 5000 Forbes Avenue Pittsburgh PA 15213-3890 Dear Prof. Marculescu: I am very pleased to write this letter of nomination for Dr. Fei Su for the AC Outstanding Ph.D. Dissertation Award in Electronic Design Automation. received his Ph.D. degree from Duke University in May 2006 and his thesis wo was carried out under my supervision. Fei’s PhD dissertation is titled “Synthesis, Testing, and Reconfigurat Techniques for Digital Microfluidic Biochips”. It is focused on design automati and test methods for emerging lab-on-a-chip devices that rely on the principle electrowetting-on-dielectric. By exploiting the reconfigurability inherent droplet-based “digital” microfluidics, these devices are revolutionizing a w range of applications, such as high-throughput sequencing, para immunoassays, blood chemistry for clinical diagnostics, DNA sequencing, a environmental toxicity monitoring. Microsystems for biomedical and sensing applications are often referred to lab-on-a-chip or biochips. These are typically centimeter-sized chips, with on-ch components having micrometer feature lengths. These components are created IC fabrication technology (surface or bulk micro-machining), and they are diverse functionality. Just as bioscience is sometimes called “wet” science, t application of biochips relies primarily on its ability to work with fluids throu its on-chip components. Biochemical samples are placed on the biochip in liquid form. A pre-programmed analysis is then carried out automatically and parallel. Miniaturization enables minute sample volume, thus it speeds chemical reactions and analytical detection; automation and parallelization ma it possible to carry out a massive number of different tests simultaneously. The characteristics, especially the delivery of results for a large number of tests with a short amount of time, are especially relevant for clinical diagnosti environmental monitoring, and bio-defense applications. Digital microfluidics has heralded the second (and remarkably advanc generation of biochips. It utilizes tiny droplets as on-chip chemical compou carriers. An on-chip array of electrodes that are individually addressable throu CMOS electronics can manipulate each droplet electrically. A set of programma CMOS instructions can induce the merging of two droplets. Such merg operations constitute the key operations in on-chip chemical reactions. Multi-s 34 The operating system manages complexity, allows multi-tasking! 17
    • Design Methodology • VLSI design Constraints: VLSI chip Area <… If constraints System Level wanted Delay <… are not met Re-design System Level Module Level Module Level ….. Gate Level Gate Level ….. Circuit Level Circuit Level ….. ….. Top-down design method Bottom-up design method 35 Biochip Design Methodology • Bottom-up vs. top-down biochip design Constraints Biochip wanted If constraints Biochip designed are not met Biochip wanted Re-design all sub-blocks ! Biochip designed Anticipated to be needed Constraints Constraints Module 1 Module m Module 1 Module m ….. designed and verified … designed and verified designed designed Constraints Constraints Anticipated to be needed Component 1 Component n Component 1 Component n ….. ….. designed and verified designed and verified designed designed Top-down design method Bottom-up design method 36 18
    • Biochip Design Automation 37 Design Automation: Biochip Synthesis • Full-custom bottom-up design Top-down system-level design S1: Plasma, S2: Serum, S3: Urine, S4: Saliva Assay1: Glucose assay, Assay2: Lactate assay, Assay3: Pyruvate assay, Assay4: Glutamate assay S1, S2, S3 and S4 are assayed for Assay1, Assay2, Assay3 and Assay4. Scheduling of operations Binding to functional resources Physical design 38 19
    • Sequencing Graph Model Sequencing graph model for multiplexed bioassays 39 Mathematical Programming Model Objective Constraints Dependency constraints • First define a binary Stj ≥ Sti + d(vi) if there is a dependency variable ⎧ 1 if operation vi starts at X ij = ⎨ between vi and vj ⎩ 0 otherwisej. time slot Resource constraints Starting time of operation vi : Reservoirs/dispensing ports T St i = ∑ j × X ij Nr reservoirs/dispensing ports assigned to j =1 each type of fluid (Nr = 1) ∑ X ij ≤ 1, ∑ X ij ≤ 1 Completion time of operation: … : 1≤ j≤ T i:vi∈I m + n i:vi ∈I1 C = max {Sti + d(vi) : vi ∈D1, …, Dn} Reconfigurable mixers and Objective function: storage units minimize C Nmixer(j) + 0.25 Nmemory(j) ≤ Na 1 ≤ j ≤ T Optical detectors Nd detectors are assigned to each bioassay (Nd = 1) j j ∑ X ij ≤ 1, …v∑ ∑ X ij ≤ 1 1≤ j≤ T ∑ i: ∈D l = j − d ( vi ) i:vi ∈D1 l = j − d ( vi ) i n1 40 20
    • Physical Design: Module Placement • Placement determines the locations of each module on the microfluidic array in order to optimize some design metrics • High dynamic reconfigurability: module placement 3-D packing modified 2-D packing Reduction from 3_D placement to a modified 2-D placement 41 Unified Synthesis Methodology 42 21
    • Protein Assay: Dilution Steps Sequencing graph model • Maximum array area: 10x10 • Maximum number of optical detectors: 4 • Reservoir number: 1 for sample; 2 for buffer; 2 for reagent; 1 for waste • Maximum bioassay time: 400 s 43 Synthesis Results Bioassay completion time T: 363 seconds Biochip array: 9x9 array 44 22
    • Synthesis Results (Cont.) • Defect tolerance Bioassay completion time T: 385 seconds (6% increase) 45 Droplet Routing • A key physical design problem for digital microfluidic biochips • Given the results from architectural-level synthesis and module placement: – Determine droplet pathways using the available cells in the microfluidic array; these routes are used to transport droplets between modules, or between modules and fluidic I/O ports (i.e., boundary on-chip reservoirs) • To find droplet routes with minimum lengths – Analogous to the minimization of the total wirelength in VLSI routing • Need to satisfy critical constraints – A set of fluidic constraints – Timing constraints: (the delay for each droplet route does not exceed some maximum value, e.g., 10% of a time-slot used in scheduling) 46 23
    • Fluidic Constraints Directly • Assume two given droplets as Di adjacent and Dj, and let Xi(t) and Yi(t) Diagonally denote the location of Di at time t adjacent How to select the admissible locations at time t +1? Rule #1: |Xi(t+1) − Xj(t+1)| ≥ 2 or |Yi(t+1) − Yj(t+1)| ≥ 2, i.e., their new locations are not adjacent to each other. Rule #2: |Xi(t+1) − Xj(t)| ≥ 2 or |Yi(t+1) − Yj(t)| ≥ 2, i.e., the activated cell for Di cannot be adjacent to Dj. Rule #3: |Xi(t) − Xj(t+1)| ≥ 2 or |Yi(t) − Yj(t+1)| ≥ 2. Static fluidic constraint Dynamic fluidic constraints 47 Experimental Verification (a) Experimental verification of Rule #1: droplets begin on electrodes 1 and 4; (b) Electrodes 2 and 3 are activated, and 1 and 4 deactivated; (c) Merged droplet. (a) Experimental verification of Rule #2: droplets begin on electrodes 2 and 4; (b) Electrodes 1 and 3 are activated, and 2 and 4 deactivated. 48 24
    • Experimental Verification (Cont.) (a) Experimental verification of Rule #3: droplets begin on electrodes 4 and 7; (b) Electrodes 3 and 6 are activated, and 4 and 7 deactivated; (c) Merged droplet. • To demonstrate that adherence to Rule #1 is not sufficient to prevent merging. Both Rule #2 and Rule #3 must also be satisfied during droplet routing. • These rules are not only used for rule checking, but they can also provide guidelines to modify droplet motion (e.g., force some droplets to remain stationary in a time-slot) to avoid constraint violation if necessary 49 Design of Pin-Constrained Biochips Direct Addressing • Each electrode connected to an independent pin • For large arrays (e.g., > 100 x 100 electrodes) – Too many control pins ⇒ high fabrication cost – Wiring plan not available PCB design: 250 um via hole, 500 um x 500 um electrode Via Holes Wires Nevertheless, we need high-throughput and low cost: DNA sequencing (106 base pairs), Protein crystallization (103 candidate conditions) Disposable, marketability, $1 per chip 50 25
    • Pin-Constrained Biochip Design • Cross-referencing Orthogonally placed pins on top and bottom plates Advantage k = n x m pins n + m pins for an n x m microfluidic array Disadvantage Suffer from electrode interference 51 Electrode Interference • Unintentional Electrode Actuation Selected column and row pins may intersect at multiple electrodes • Unintentional Droplet Manipulation 1 2 1 3 Unintentional 3 4 destination cells 5 6 7 destination cells 8 2 9 10 1 2 3 4 5 6 7 8 9 10 52 26
    • Efficient (Concurrent) Droplet Manipulation • Goal: Improve droplet manipulation concurrency on cross-referencing-based biochips. 9 steps needed if moving one droplet at a time (too slow) 53 Efficient Droplet Manipulation • Observation – Droplet manipulations whose destination cells belongs to the same column/row can be carried out without electrode interferences. 4 9 destination cells 54 27
    • Efficient Droplet Manipulation • Methodology – Group droplet manipulations according to their destination cells – All manipulations in a group can be executed simultaneously The goal is to find an optimal grouping plan which results in the minimum number of groups. 55 Efficient Droplet Manipulation • Problem formulation Destination cells Nodes Destination cells in one column/row a clique Grouping Clique partitioning Optimal grouping Minimal clique-partitioning (NP-Complete) 56 28
    • Broadcast Electrode-Addressing • Observation “Don’t-Cares” in Electrode-Actuation Sequences Electrode control inputs: 3 values “1” –- activated “0” –- deactivated “x” –- can be either “1” or “0” Therefore, activation sequences can be combined by interpreting “x” Floating electrode Example: A droplet routed Corresponding electrode activation counterclockwise on a loop of electrodes sequences 57 Solution Based on Clique Partitioning • Idea – Combining compatible sequences to reduce # of control pins • Clique partitioning based method Electrodes Nodes Electrodes with compatible activation sequences a clique Optimal combination Minimal clique-partitioning 58 29
    • Solution Based on Clique Partitioning Reduced number Bioassay synthesis of control pins results Extract Combine Scheduling & Clique partitioning droplet routing plan Result Calculate Generate Map Activation sequence Undirected graph for each electrode 59 Application to a Multiplexed Bioassay A biochip target execution of a Sequencing graph model of the multiplexed assay multiplexed assay • A glucose assay and a lactate assay based on colorimetric enzymatic reactions • 4 pairs of droplets – {S1, R1}, {S1, R2}, {S2, R1}, {S2, R2}, are mixed in the mixer in the middle of the chip, the mixed droplets are routed to the detector for analysis 60 30
    • Results 132 s 73 s 73 s Comparison of bioassay completion time using different addressing methods Addressing Broadcast Array-partitioning- Cross-referencing- methods addressing based method based method # of control pins 25 35 30 61 Application to Multi-functional Chip • Multi-functional Chip – biochips targeting the execution of a set of (multiple) predetermined bioassays • Application of Broadcast Addressing to Multi-functional Chips Key idea: treat the union of the target bioassays as a single bioassay – Collect droplet routing and schedule information for each target bioassay – Calculate activation sequences for each bioassay – Merge the activation sequences from the different assays and obtain a collective activation sequence for each electrode – Note that merging of activation sequences can be carried out in any arbitrarily-chosen order 62 31
    • Addressing Results Sequencing graph model of the multiplexed assay Sequencing graph model of protein dilution Sequencing graph model of Polymerase Chain Reaction (PCR) 63 Addressing Results Chip layout and broadcast- addressing result for the multi-functional chip for 1. Multiplexed assay 2. PCR assay 3. Protein dilution assay Total number of control pins: 37 The addition of two assays to the biochip for the multiplexed assay leads to only 13 extra control pins 64 32
    • Reconfigurability • Common microfluidic operations – Different modules with different performance levels (e.g., several mixers for mixing) – Reconfiguration by changing the control voltages of the corresponding electrodes 65 Reconfiguration and Graceful Degradation • Reconfigure the faulty module – Avoid defects (faulty cells) • Reconfiguration: bypass faulty cells – No spare cells; use fault-free unused cells • Defect tolerance in design procedure (increase in design complexity) – Incorporate physical redundancy in the array • Spare cells replace defective cells (local reconfiguration, application-independent) • 66 33
    • Testing of Digital Microfluidics Biochips Stimuli: Test droplets; Response: Presence/absence of droplets No. Cause of Defect Fault Observable defect type cells model error Dielectric Droplet-electrode Droplet undergoes Excessive 1 short (short between actuation breakdown electrolysis; prevents the droplet and the voltage further applied to electrode) transportation electrode Irreversible Electrode-stuck-on Unintentional droplet Electrode 1 charge (electrode remains actuation for operations or concentration on excessive constantly activated) stuck droplets duration electrode Excessive Misalignment of Pressure gradient (net 1 Droplet transportation mechanical parallel plates static pressure in without activation voltage force applied (electrodes and some direction) to chip ground plane) Non-uniform Fragmentation of droplets Coating 1 Dielectric islands (islands of Teflon and their motion is failure dielectric layer coating) prevented 67 More Defects in Digital Microfluidic Biochips No. Fault Cause of Defect Observable defect type cells model error Floating droplets Abnormal Grounding 1 Failure of droplet (droplet not metal layer failure transportation deposition anchored ) and etch Electrode open Broken wire to 1 Failure to activate the variation (actuation not Control source electrode for droplet during possible) transportation fabrication Electrode short Metal connection 2 A droplet resides in the (short between middle of the two shorted between adjacent electrodes, and its transport electrodes electrodes) cannot be achieved Particle Particle connects 2 Electrode short contamination or two adjacent liquid residue electrodes Protein Sample residue Resistive open at Droplet transportation is 1 absorption on electrode electrode impeded. during bioassay surface Assay results are outside the Contamination range of possible outcomes 68 34
    • Electrical Detection Mechanism 10 K Output Periodic • Minimally invasive 150 pF square • Easy to implement (alleviate 74C14 waveform the need for external devices) Droplet +5V 1N914 • Fault effect should be unambiguous 5K 1N5231 5.1V 1N914 Electrically control and track test stimuli droplets Gnd Capacitive changes reflected in electrical signals (Fluidic • If there is a droplet, domain to electrical domain) output=1; otherwise, output=0 • Fault-free : there is a droplet between sink electrodes Faulty: there is no droplet. 69 Defect-Oriented Experiment • Understand the impact of certain defects on droplet flow, e.g., for short-circuit between two electrodes • Experimental Setup – To evaluate the effect of an electrode short on microfluidic behavior 70 35
    • Conclusions • Digital microfluidics offers a viable platform for lab-on-chip for clinical diagnostics and biomolecular recognition • Design automation challenges – Automated synthesis: scheduling, resource binding, module placement; droplet routing; testing and reconfiguration • Bridge between different research communities: bioMEMS, microfluidics, electronics CAD and chip design, biochemistry • Growing interest in the electronics CAD community – Special session on biochips at CODES+ISSS’2005 (appears in CFP now) – Special issue on biochips in IEEE Transactions on CAD (Feb 2006), IEEE Design & Test of Computers (Jan/Feb’07) – Workshop on biochips at DATE’06 – Tutorials on digital microfluidic lab-on-chip at DATE’07, ISCAS’08, VDAT 2007; embedded tutorial at VLSI Design 2005 – Other notable activities in digital microfluidics: University of California at Los Angeles, University of Toronto, Drexel University, IMEC (Belgium), Freiburg (Germany), Philips (Netherlands), Fraunhofer Institute (Berlin, Germany), and many more…. 71 72 36
    • ISBN: 0849390095 2006 X, 406 p. Hardcover Publication Date: 10/5/2006, 248 p. ISBN-10: 1-4020-5122-0, ISBN-13: 978-1-4020-5122-7 73 37