Computer-Aided Design of Microfluidic Very Large Scale Integration (mVLSI) Biochips

147 Pages·2017·6.761 MB·english

by  Kai Hu, Krishnendu Chakrabarty, Tsung-Yi Ho

Kai Hu Krishnendu Chakrabarty
(cid:129)
Tsung-Yi Ho
Computer-Aided Design
fl
of Micro uidic Very Large
Scale Integration (mVLSI)
Biochips
Design Automation, Testing,
and Design-for-Testability
123
KaiHu Tsung-Yi Ho
Oracle, Inc. Department ofComputer Science
SantaClara, CA National TsingHua University
USA Hsinchu
Taiwan
Krishnendu Chakrabarty
Department ofECE
Duke University
Durham, NC
USA
ISBN978-3-319-56254-4 ISBN978-3-319-56255-1 (eBook)
DOI 10.1007/978-3-319-56255-1
LibraryofCongressControlNumber:2017935833
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Preface
Flow-based microfluidic biochips constitute an emerging technology for the
automation of biochemical procedures. Recent advances in fabrication techniques
have enabled the development of these devices. Increasing integration levels pro-
videbiochipswithtremendouspotential;alargenumberofbioassays,i.e.,protocols
for biochemistry, can be processed independently, simultaneously, and automati-
cally on a coin-sized microfluidic platform. However, the increase in integration
level introduces new challenges in the design optimization and testing of these
devices, which impede their further adoption and deployment.
This book is focused on enhancing the automated design and the use of
flow-based microfluidic biochips and on developing a set of solutions to facilitate
thefullexploitationofdesigncomplexitiesthatarepossiblewithcurrentfabrication
techniques. Four key research challenges are addressed in the book; these include
design automation, wash optimization, testing, and defect diagnosis.
Despite the increase in the number of on-chip valves, designers are still using
full-custommethodologiesinvolvingmanymanualstepstoimplementthesechips.
Since these chips can easily have thousands of valves, a manual design procedure
canbetime-consuminganderror-prone,anditcanresultininefficientdesigns.This
book presents the first problem formulation for automated control-layer design in
flow-based microfluidic biochips and describes a systematic approach for solving
this problem. Our goal is to find an efficient routing solution for control-layer
design with a minimum number of control pins.
Theproblemofcontaminationremovalinflow-basedmicrofluidicbiochipsmust
also be addressed. Applications in biochemistry require high precision to avoid
erroneous assay outcomes, and they are vulnerable to contamination between two
fluidicflowswithdifferentbiochemistries.Thisbookproposesthefirstapproachfor
automatedwashoptimizationforcontaminationremovalinflow-basedmicrofluidic
biochips. The proposed approach ensures effective cleaning and targets the gen-
eration of wash pathways to clean all contaminated microchannels with minimum
execution time under physical constraints.
Another practical problem addressed in this book is the lack of test techniques
forscreeningdefectivebiochipsbeforetheyareusedforbiochemicalanalysis.This
vii
viii Preface
bookpresentsanefficientapproachforautomatedtestingofflow-basedmicrofluidic
biochips.Thetesttechniqueisbasedonabehavioralabstractionofphysicaldefects
in microchannels and valves. The flow paths and flow control in the microfluidic
devicearemodeledasalogiccircuitcomposedofBooleangates,whichallowstest
generation to be carried out using standard automatic test-pattern generation tools.
Based on the analysis of untestable faults in the logic circuit model, we present a
design-for-testability technique that can achieve 100% fault coverage.
Finally, this book presents a technique for the automated diagnosis of leakage
andblockagedefects.Theproposedmethodtargetstheidentificationofdefecttypes
andtheirlocationsbasedontestoutcomes.Itreducesthenumberofpossibledefect
sites significantly while identifying their exact locations.
In summary, this book provides a set of optimization and testing methods for
flow-based microfluidic biochips. These methods are expected to not only shorten
the product development cycle, but also accelerate the adoption and further
development of this emerging technology by facilitating the full exploitation of
design complexities that are possible with current fabrication techniques.
Santa Clara, CA, USA Kai Hu
Durham, NC, USA Krishnendu Chakrabarty
Hsinchu, Taiwan Tsung-Yi Ho
Contents
1 Introduction.... .... .... ..... .... .... .... .... .... ..... .... 1
1.1 Introduction of Microfluidic Biochip Platforms .. .... ..... .... 2
1.2 Overview of Flow-Based Microfluidic Biochips.. .... ..... .... 6
1.2.1 Structure and Fabrication . .... .... .... .... ..... .... 7
1.2.2 Components .. ..... .... .... .... .... .... ..... .... 8
1.2.3 Applications .. ..... .... .... .... .... .... ..... .... 12
1.3 Challenges and Motivation.. .... .... .... .... .... ..... .... 15
1.3.1 Design Automation.. .... .... .... .... .... ..... .... 16
1.3.2 Contamination Removal .. .... .... .... .... ..... .... 16
1.3.3 Defects and Erroneous Operations .. .... .... ..... .... 17
1.4 Outline of the Book .. ..... .... .... .... .... .... ..... .... 19
References.. .... .... .... ..... .... .... .... .... .... ..... .... 20
2 Control-Layer Optimization.... .... .... .... .... .... ..... .... 25
2.1 Motivation and Related Prior Work ... .... .... .... ..... .... 26
2.2 Problem Description, Design Requirements, and Challenges . .... 26
2.2.1 Pressure-Propagation Delay.... .... .... .... ..... .... 26
2.2.2 Requirements in Control-Layer Design... .... ..... .... 27
2.2.3 Valve Addressing ... .... .... .... .... .... ..... .... 30
2.2.4 Routing of Control Channels... .... .... .... ..... .... 33
2.2.5 Placement of Control Pins. .... .... .... .... ..... .... 34
2.2.6 Relationship Between Control-Layer Optimization
and Clock-Tree Design in VLSI Circuits . .... ..... .... 34
2.3 Problem Formulation . ..... .... .... .... .... .... ..... .... 35
2.4 Algorithm Design.... ..... .... .... .... .... .... ..... .... 36
2.4.1 Routing Algorithm 1. .... .... .... .... .... ..... .... 37
2.4.2 Routing Algorithm 2. .... .... .... .... .... ..... .... 41
2.5 Experimental Results . ..... .... .... .... .... .... ..... .... 45
2.5.1 Experiments with Two Fabricated Biochips ... ..... .... 46
2.5.2 Experiments with Synthetic Benchmarks.. .... ..... .... 50
xi
xii Contents
2.6 Conclusions .... .... ..... .... .... .... .... .... ..... .... 50
References.. .... .... .... ..... .... .... .... .... .... ..... .... 51
3 Wash Optimization for Cross-Contamination Removal .. ..... .... 53
3.1 Motivation and Challenges.. .... .... .... .... .... ..... .... 54
3.2 Problem Description and Formulation . .... .... .... ..... .... 55
3.2.1 Physical Implementability of a Wash Path .... ..... .... 55
3.2.2 Execution Time for a Wash Path ... .... .... ..... .... 57
3.3 Search for a Set of Washing Paths.... .... .... .... ..... .... 62
3.3.1 Generation of the Path Dictionary... .... .... ..... .... 62
3.3.2 Storage of the Path Dictionary . .... .... .... ..... .... 64
3.3.3 Identification of Washing-Path Set .. .... .... ..... .... 65
3.3.4 Washing of Multiple Contaminant Species.... ..... .... 69
3.3.5 Complexity Analysis. .... .... .... .... .... ..... .... 71
3.4 Results: Application to Fabricated Biochips. .... .... ..... .... 72
3.4.1 Results for ChIP.... .... .... .... .... .... ..... .... 73
3.4.2 A Programmable Microfluidic Device
with an 8-by-8 Grid . .... .... .... .... .... ..... .... 77
3.5 Conclusions .... .... ..... .... .... .... .... .... ..... .... 78
References.. .... .... .... ..... .... .... .... .... .... ..... .... 79
4 Fault Modeling, Testing, and Design for Testability. .... ..... .... 81
4.1 Motivation and Challenges.. .... .... .... .... .... ..... .... 82
4.2 Defects and Fault Modeling. .... .... .... .... .... ..... .... 83
4.3 Testing Strategy . .... ..... .... .... .... .... .... ..... .... 86
4.4 Applications to Fabricated Biochip.... .... .... .... ..... .... 90
4.4.1 Logic Circuit Model . .... .... .... .... .... ..... .... 90
4.4.2 Test-Pattern Generation and Results . .... .... ..... .... 91
4.5 Automated Generation of Logic-Circuit Model... .... ..... .... 92
4.5.1 Physical Representation of Boolean Gates in Netlists. .... 92
4.5.2 Hierarchical Modeling.... .... .... .... .... ..... .... 94
4.5.3 Fault Analysis Based on ATPG Results .. .... ..... .... 97
4.6 Other Practical Concerns ... .... .... .... .... .... ..... .... 97
4.6.1 Test Cost. .... ..... .... .... .... .... .... ..... .... 98
4.6.2 Dynamic Faults..... .... .... .... .... .... ..... .... 98
4.6.3 Multiple Faults ..... .... .... .... .... .... ..... .... 99
4.7 Experimental Demonstration. .... .... .... .... .... ..... .... 99
4.7.1 Experimental Feasibility Demonstration .. .... ..... .... 99
4.7.2 Pattern Set-up Time, Measurement Time
and Refresh Time ... .... .... .... .... .... ..... .... 101
4.7.3 Experimental Demonstration I: Cell Culture Chip.... .... 103
4.7.4 Experimental Demonstration II: WGA Chip... ..... .... 106
Contents xiii
4.8 Untestable Faults and Design-For-Testability .... .... ..... .... 107
4.8.1 Causes of Untestable Faults ... .... .... .... ..... .... 109
4.8.2 DfT for Flow-Based Microfluidic Biochips.... ..... .... 110
4.8.3 Demonstration of Proposed DfT Approach.... ..... .... 112
4.9 Conclusion. .... .... ..... .... .... .... .... .... ..... .... 114
References.. .... .... .... ..... .... .... .... .... .... ..... .... 114
5 Techniques for Fault Diagnosis . .... .... .... .... .... ..... .... 117
5.1 Motivation and Challenges.. .... .... .... .... .... ..... .... 117
5.2 Problem Description.. ..... .... .... .... .... .... ..... .... 118
5.2.1 Single-Defect-Type Assumption .... .... .... ..... .... 118
5.2.2 Syndrome Analysis.. .... .... .... .... .... ..... .... 119
5.2.3 Formulation as a Hitting-Set Problem.... .... ..... .... 121
5.3 Algorithm Design.... ..... .... .... .... .... .... ..... .... 127
5.3.1 Complexity Analysis. .... .... .... .... .... ..... .... 130
5.4 Results: Application to Fabricated Biochips. .... .... ..... .... 131
5.4.1 Results for ChIP Chip.... .... .... .... .... ..... .... 132
5.4.2 Results for WGA Chip... .... .... .... .... ..... .... 133
5.4.3 Results for Cell Culture Chip .. .... .... .... ..... .... 135
5.5 Conclusion. .... .... ..... .... .... .... .... .... ..... .... 136
References.. .... .... .... ..... .... .... .... .... .... ..... .... 136
6 Conclusions and New Directions .... .... .... .... .... ..... .... 137
6.1 Conclusions .... .... ..... .... .... .... .... .... ..... .... 137
6.2 Opportunities and New Directions .... .... .... .... ..... .... 138
6.2.1 Fault-Tolerant Design .... .... .... .... .... ..... .... 139
6.2.2 Programmable Multipurpose Biochip Platforms ..... .... 139
6.2.3 Balancing Control Channels by Modifying Channel
Width ... .... ..... .... .... .... .... .... ..... .... 139
6.2.4 Washing in Parallel.. .... .... .... .... .... ..... .... 140
6.2.5 Testing for Dynamic Defects... .... .... .... ..... .... 140
References.. .... .... .... ..... .... .... .... .... .... ..... .... 140
Index .... .... .... .... .... ..... .... .... .... .... .... ..... .... 141
Chapter 1
Introduction
Microfluidicsdealswiththeprecisetransportationoffluids(liquidsorgases)insmall
amounts,e.g.,microliters,nanolitersorevenpicoliters[1].Itisadvancingatarapid
pace and researchers have demonstrated numerous practical applications over the
past decade. As our ability to fabricate structures at the microscale and nanoscale
levelhasprogressed,microfluidics-basedbiochips,alsoknownaslab-on-a-chip,have
developedrapidlytoenablethestudyofcellularprocessesatascalecompatiblewith
cellsize.Thesebreakthroughsarerevealingnewinformationabouttheoperational
responsesofcells,includinghowtheyrespondtophysicalandchemicalcuesfrom
theirimmediateenvironment.
Owning to their higher sensitivity, smaller size, and lower cost, microfluidics-
basedbiochipsrecentlyhavebeendevelopedforvariousbioassays,suchasthediag-
nosisofdiseases[2],real-timeDNAsequencing[3],andantigendetection[4].These
highlyintegratedmicrosystemsarecapableofmanipulatingandanalyzingpicoliter
volumes of samples and reagents to realize various operations according to a cus-
tomized assay plan provided by users. Moreover, developments in many of these
applicationshaveoftenbeenfacilitatedbythetechnologicaladvancementsinopti-
calandotherdetectiontechnologies,therebyenablingtheuserstoobtainmeasured
datawithunprecedentedaccuracy,precision,andsensitivity[5, 6].
Microfluidics-based biochips constitute a multidisciplinary field that involves
engineering,biotechnology,microfabrication,etc.In1980,Terryetal.firstreported
a chip in which a gas-chromatography column was fabricated on a silicon wafer.
In 1990, Manz demonstrated the first practical microfluidic biochip, referred to as
“Micrototal analysis system” (µTAS) [7], which can integrate and automate the
wholerangeofchemicalanalysisprocessstepsusingmicrofluidiccomponents,pri-
marilybasedonIC-likemicrofabricationprocessesandmaterials.Morethan35,000
papershavethusfarpublishedonthetopicofmicrofluidics,andthepublicationcount
isrisingsteadily[8].Inaddition,over8,240patentsreferringtomicrofluidicshave
beenissuedonlyintheUS[9].Advancesinacademicresearchhavebeenfollowed
byasuccessinthecommercialmarketplace.In2011,Fluidigm,abiotechcompany
©SpringerInternationalPublishingAG2017 1
K.Huetal.,Computer-AidedDesignofMicrofluidicVeryLargeScale
Integration(mVLSI)Biochips,DOI10.1007/978-3-319-56255-1_1
2 1 Introduction
that focuses on flow-based microfluidic biochips, launched its initial public offer-
ingatNASDAQ,whichrepresentedasignificantmilestoneinthematurationofthe
microfluidicindustry.AccordingtoareportreleasedbyResearchandMarketsinJune
2013,theglobalbiochipmarketwillgrowfrom$1.4billionin2013to$5.7billion
by2018[10].InJuly2013,AdvancedLiquidLogic,abiotechcompanyfocusedon
digitalmicrofluidicstechnology,wasacquiredbyIllumina,Inc.(NASDAQ:ILMN)
for$95million[11].
Microfluidic devices offer many advantages over conventional biochemical
devices and analyzers [12]. The inherent advantages of scaling down include
increasedspeed,efficiency,areductioninthedemandforsampleandreagents,and
thepotentialformultiplexingandparallelization.Thesebenefitsaredetailedbelow.
• Thelowvolumeofexpensivereagentsandhard-to-obtainsamplesmanipulatedin
thesedevicescansignificantlyreducethecostsassociatedwithexperiments.
• Theprecisecontrolofreactionscontrolofreactionsandultra-sensitivedetection
onmicrofluidicdevicescanenhancetheaccuracyofexperiments.
• The time required for chemical reactions to occur at the nanoliter scale can be
greatlyreducedduetothehighsurface-to-volumeratios.
• Highthroughputcanbeachievedforexperimentsasmicrofluidicplatformshave
thecapabilityofintegratingseveralfastandefficientoperationsonthesamechip.
1.1 IntroductionofMicrofluidicBiochipPlatforms
Thereareseveraltypesofmicrofluidicbiochipplatforms,eachhavingitsownadvan-
tagesandlimitations[15].Basedonthemethodsusedtomanipulatetheliquidonthe
chip,biochipscanbegenerallyclassifiedintotwocategories:digital(droplet-based)
microfluidicbiochips(DMF)andcontinuous(flow-based)microfluidicbiochips.An
examplelayoutfortheformerisshowninFig.1.1a;anexamplelayoutforthelatter
isshownFig.1.1b.
Indroplet-basedmicrofluidicbiochips(DMF),allmolecularprocessesandbio-
chemicalreactionsarecarriedoutusingasdiscretedropletswithpicolitervolumeson
anarrayofdiscreteunitcells[17].Thecontrolofdropletsisbasedonelectrowetting-
on-dielectric(EWOD)[18].TheprincipleofEWODisthatanappliedelectricfieldat
adropletwillincreasethecontactangleandcontactareaofthedropletandtherefore
tendtopullitdownontoelectrode.Ifavoltageisappliedontheadjacentelectrodes,
unbalanced electric field and contact angle will be created and the droplet will be
forcedtomovetotheactivatedelectrode(Fig.1.2).Hence,fluid-handlingoperations,
suchasdilutionofsamplesandreagents,mixing,incubation,andtransportationof
droplets,canbeimplementedonthedigitalmicrofluidicplatformbyapplyingappro-
priatevoltagestotheelectrodes[16,19](Fig.1.3).Thesequenceofactuationvoltages
ispre-determined(i.e.,beforetheimplementationoffluid-handlingoperations),and
they are stored in a microcontroller or in computer memory. Under clock control,
themicrocontrollertransferspreloadedactuationdatatothebiochip[20].
1.1 IntroductionofMicrofluidicBiochipPlatforms 3
Fig. 1.1 Example layouts of two types of microfluidic biochips: a digital (droplet-based)
biochips[13];bcontinuous(flow-based)biochips[14]
In digital microfluidic biochips (DMF), each droplet is controlled individu-
ally. Thus, various processes can be performed simultaneously with a simple and
compactdesign.Becausedropletsaremanipulatedongenericarraysofelectrodes,
droplet operations are reconfigurable from experiment to experiment. It is also