高性能生物傳感集成電路 High-Performance Bio-sensing ICs

High-PerformanceBio-sensingICs

SinaFarajiAlamoutiRikkyMuller

ElectricalEngineeringandComputerSciencesUniversityofCalifornia,Berkeley

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May1,2025

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Acknowledgement

Writingthisdissertation,IwholeheartedlybelievethatgettingaPhDisa

journeythatcomprisesofmanystepsandisaboutgoingoutsideofone'scomfortzone,makingcriticalbutwell-informeddecisionsontheroutestotake,communicatingwithpeopleinsideandoutsidethecircleofpeers,

dedicatingthetime,potentiallypullingall-nighters,andfailing,andfailingandfailing,tofinallysucceed.Navigatingthroughthisjourneywas

impossiblewithoutthehelpofaguide,soI'dliketothankRikkyforallthesupportthroughoutthistime.

I'dliketothankmanyofmycolleaguesandgroup-mateswhogenuinelymadeiteasierformetosurviveandsucceed.

Andofcourseaboveall,Iwouldn'tbeheretodayifitwasnotforthesupportofmydearGoliandmyfamily.Thankyou!

High-PerformanceBio-sensingICs

by

SinaFarajiAlamouti

Adissertationsubmittedinpartialsatisfactionofthe

requirementsforthedegreeof

DoctorofPhilosophy

in

ElectricalEngineeringandComputerSciences

inthe

GraduateDivision

ofthe

UniversityofCalifornia,Berkeley

Committeeincharge:

ProfessorRikkyMuller,ChairProfessorJanRabaey

ProfessorLydiaSohn

Fall2022

ThedissertationofSinaFarajiAlamouti,titledHigh-PerformanceBio-sensingICs,isap-proved:

Chair

Prof.RikkyMuller

Prof.JanRabaey

Prof.LydiaSohn

Date

Date

Date

01/13/2023

01/13/2023

01/13/2023

UniversityofCalifornia,Berkeley

High-PerformanceBio-sensingICs

Copyright2022

by

SinaFarajiAlamouti

1

Abstract

High-PerformanceBio-sensingICs

by

SinaFarajiAlamouti

DoctorofPhilosophyinElectricalEngineeringandComputerSciences

UniversityofCalifornia,Berkeley

ProfessorRikkyMuller,Chair

Recordingofbio-signalsfromthehumanbodyhasundergonesigni?cantimprovementsintermsofpower,speed,andformfactorinthepastdecadeduetothehelpoflow-costcom-pactIC-basedsolutions.RecentdevelopmentofthesedevicesfocusonintegratingmultiplesensorinputsinasingleIC,enhancingtherobustnessofthesensorinthefaceofchallengesinambulatorysettings,aswellasincludingsomelevelofsmartnessinthesensoroperationtoimproveitsperformance.Inthisdissertation,acoupleofnovelexamplesoftheaboveICsarepresentedthatachievestate-of-the-artperformancewhiledeliveringthetargetfunction-ality.Inthe?rstchapter,aheart-rateandoxygensaturationmonitoringICisproposedthatleveragesasparsesamplingalgorithmtosigni?cantlylowerthesensorpowerconsumptionandincreasebatterylife.Theninchapter3asensorICisdiscussedthatutilizesbody-sensorimpedanceinformationtohelpcombattheimpactofusers’motionartifactinbiopotentialrecordings.Lastly,anultra-lownoisecurrentsensorICiscoveredinchapter4thatenablesmulti-channelsensingofverysmallelectricalcurrentsinbiomedicalapplications.Theper-formanceoftheabovesensorICsarecomparedagainstpriorartsandfuturedirectionsoftheseprojectsarediscussedattheendofeachchapter.

i

Tothatwhosecompanyeasesthetroubles.

ii

Contents

Contentsii

ListofFiguresiv

ListofTablesviii

1Biosensors,GoalsandChallenges1

1.1HistoryofBiosensing 1

1.2BiosensorInterfaces 1

1.3BiosensingICs 4

2LowPowerHR&SpO2Sensing6

2.1Motivation 6

2.2Devices,Methods,andPriorArts 7

2.3SystemOverview 8

2.4SparseSamplingofPPGSignal 10

2.5CircuitImplementation 13

2.6MeasurementResults 18

2.7Summary&Comparison 26

3Electrode-SkinImpedanceMeasurement28

3.1Motivation 28

3.2SystemOverview 31

3.3ESIRecordingIC 36

3.4Measurementresults 42

3.5ComparisonwithPriorArts 44

3.6FutureWork 46

4LowNoiseCurrentSensing48

4.1Introduction 48

4.2SensorOverview 52

4.3CurrentSensorIC 56

4.4MeasurementResults 57

iii

4.5SummaryandComparison 58

4.6FutureDirections 60

4.7FinalThoughts 67

Bibliography69

ANoiseanalysis,CTIAvs.ZTIA75

BI/QDemodulationinImpedanceAcquisition78

CCurrentSensorsFigureofMerit80

C.1CTIAFoM 80

C.2GenericTIAFoM 83

C.3I-to-DconvertersFoM 85

DAnalysisofRampOversampling86

iv

ListofFigures

1.1DrysnapelectrodesfabricatedbyFloridaResearchInstrumentsandgenericwet

electrodesusefulforbiopotentialmeasurements 2

1.2AppleWatchheartrateandbloodoxygensensorsonitsbackconsistingoffour

LEDsandfourphotodiodes 3

1.3NumberofpaperspublishedinISSCCwithafocusonbiomedicalapplications

from2006to2023 5

2.1Re?ectancemodepulseoximetryandtypicalSpO2sensingICblockdiagram 7

2.2PPGsignalACandDCcomponents.RedandIRsignalsarecomputedafter

subtractingtheAMBsample,performingsystemlevelCDS 10

2.3Sparsesamplingalgorithm.Thesensortransitionstosparsemodeafterlearning

TovermultiplecycleswhereitpredictsnextPAVs 11

2.4Sparsesamplingalgorithm?ow-chart.Thesystembeginswithcontinuousmode

samplingatfs=100Hzandthentransitionstosparsemodeuponlearningthe

period,T 12

2.5SimulatedsparsemodeHRerrorforinputsinewavesatdiferentfrequenciesand

SNRs.Greenshadesshowthe±3σrangeoftheerror 13

2.6Detailedchannelblockdiagramofthechip.TheLEDdrivermoduleishighlighted

toindicatetheuseofHVdevicesthatsupportupto8Vofsupply 14

2.7Detailedtimingdiagramoftheoperationofthesensor.Everysetofsamples

consistsofthreephases,red,IR,andambient 15

2.8SchematicoftheZTIAcoreOTA.Currentreusetopologyenhancescurrente?-

ciencyandreducespower.Nestedcommon-modefeedbacknetworksstabilizethe

OTAincommonmode,enhancingCMRR 16

2.9Post-extractionsimulatedTIAbandwidthandphasemarginvs.CPar.TheOTA

showsaminimumof62。ofphasemarginovertheentireCParrange 16

2.10TheschematicoftheHVLEDdriverisshown.Theentireunitispowergateto

savepowerinbetweenthesamples 18

2.11Thedrivercurrentwaveformupto16mAwhendrivinganOLEDwithupto5.7

nFofCPar 19

2.12Chipmicrograph(a).TFEpowerbreakdownincontinuousmode.(b)System

powerreductionbetweencontinuousmodeandsparsemode.(c) 19

v

2.13Electricaltestingresults.(a)IRNspectrumfora40pFCPar.(b)IntegratedIRN

over5Hzbandwidthvs.CPar.Thereddotcorrespondstothespectrumfrom

(a).(c)ADCoutputspectrumfora2Hzsinewaveinput 21

2.14MeasuredsparsemodeHRerrorwithinputsinewavesatdiferentfrequencies

andattwolevelsofSNR.Blackcirclesshowtheaverageerrorateachrate.The

computederroristhemeanabsoluteerrorover8swindows 22

2.15Invivoveri?cationofthesensorICinbothcontinuousandsparsemodesusing

commercialsiliconPDandLEDs.ThePPGwaveformanditscorrespondingHR

andSpO2measurementsareshown 23

2.16HRandSpO2measurementsfromasetof30recordingsusingsiliconPDand

LEDs.Thereddottedlineshowsthe?tline 23

2.17Invivoveri?cationofthesensorICinbothcontinuousandsparsemodesus-

ingorganicinterfacedevices(OLEDs&OPDs).ThePPGwaveformandthe

correspondingHRandSpO2measurementsareshown 24

2.18Invivorecordingmeasurementsetup.DataistransferredtoaPCviaanFPGA.24

2.19AsummaryoftheInvivoresultswhenusing(b)commercialsilicondevicesand

(c)?exibleorganicdevices 25

2.20MissingPAVsduetomotionartifactcreatingalargechangeinthesampledPAV

values.ThebackendinitiallyexpandsW,thenrevertstothecontinuousmode

and?nallyre-gainslockonthePPG 25

3.1EEGsignalanditsdiferentfrequencybands.AnetworkofEEGrecording

scalpelectrodesareshownontherightinaphotoadaptedfromanarticleon

MayoClinic.[18] 29

3.2SamplerecordingofEar-EEGusingdryin-earelectrodeswherethesubjectchews

anappleinthemiddleoftherecording.Largein-bandmotionartifactduetothe

subjectchewingisobservedcompletelydisruptingthemeasurement 30

3.3(a)TheESIandEarEEGrecordingsystemdiagramshowingtwoelectrodesand

thereadoutIC.(b)TheESIofdryin-earelectrodesfrom[29] 32

3.4SignalFlowdiagramofthesensorreadout.Thestimulationblockcreatesadif-ferentialACcurrentthatterminatesovertheESIinducingavoltagesignalatthestimulationfrequencyband.Thissignalisthensummedwiththedesiredbiopo-tentialsignal,heretheEEG,andisthenacquiredviaasharedfrontendADC.Inthebackend,thedecimatedADCoutputismultipliedbyin-phaseandquadra-turecomponentsofthestimulationsignalallowingfortheimpedancemagnitude

|ZES(j!)|andphase6ZES(j!)tobeextracted...................33

3.5Simplesquarewavedriveanddemodulationhardware.(a)Thedriveisimple-

mentedusingtwocurrentsourcesandabutter?yswitchtogglingatfZ.(b)The

digitaldemodulationisassimpleasasign?ipinphasewiththedemodulation

signal 35

3.6Errorsinducedintheabsolutevalueoftheimpedanceduetohigherorderhar-

monicsampling.3rd-harmonic-freedrivereducesthiserrorbymorethan50% 36

vi

3.73rdHarmonicfreedrivevs.squarewavedrive.(a)Thetimedomainwaveforms

areshownforfZ=1.6kHz.(b)Thespectrumsassociatedwith“a” 36

3.8Theschematicandtimingdiagramoftheactivestimulationunit.Thetiming

signalsaregeneratedon-chipusingthereferencemasterclock 38

3.9Thespectrumsofthestimulationcurrentsourcesat10nAofdrivecurrent.Blueandorangecurvesshowstheanalyticalandsimulatedspectraofthedrivecurrentwithoutchoppingwhere?ickernoisedominatesthenoise?ooroverEEGband-

width.Theblackcurverepresentsthedrivecurrentspectrumwithchoppingat

8kHzthatresultsinasigni?cantlyreducedcurrentnoisedensityovertheEEG

band 39

3.10OutputImpedanceoftheactivestimulationunit.Theunitmaintainsa>1GΩ

outputimpedanceacrosstheentireEEGbandwidthandevenupto3kHz 39

3.11Passivestimulationunitblockdiagram.Enableswitchescanfullydisengagethis

unitfromtheinputs,providingisolationaswellasincreasingtheinputimpedance

whenusingactivemode 40

3.12Comparisonofoutputimpedancebetweenactiveandpassivestimulationunits 41

3.13ChipmicrographofEarEEGICandthepowerbreakdownofthestimulationunit.42

3.14Thelayoutofthestimulationblock.Theareaisdominatedbytheareaofthe

passiveelements,CACandRN,P 43

3.15Oscilloscopecapturesofstimulationwaveformsover100kΩtestresistances.(a)

showsthetimedomainsquarewavestimulationwaveformat2kHz.(b)shows

thespectrumof“a”witha3rdharmonicamplitude~10dBbelowthemain

harmonic.(c)presentsthetimedomain3rdharmonicfreewaveformat2kHz.

(d)exhibitsthespectrumof“c”withthe3rdharmonicamplitudeattenuatedby

>60dB.........................................44

3.16Resistiveimpedancemeasurementresultsforarangeof~1kΩ-2MΩof

diferentialtestresistance.(a)Twopointcalibrationisperformedonthevalues

andthe?t-lineisplottedinsolidred.(b)Theμ/σofthemeasurementindB

signifyingamaximum~68dBofSNR 45

3.17EfectiveimpedancenoisespectrumoftheESIreadoutat17nAofstimulation

amplitude 45

3.18(a)Theelectrodemodelconsistingofseriesresistance(RS),doublelayercapac-itance(CDL)andtheparallelresistance(RCT).Thevaluesofeachelementineverymodelispresentedinthetable.ThesevaluesaremeasuredwithabenchtopLCRmeter.(b)Theacquiredimpedance(solidline)oftheelectrodemodelvs.

theLCRmeterreportedimpedances(dashedline).................46

4.1Applicationsforlownoisecurrentsensors.(a)Recordingofionchannelcurrentsviapatch-clampsinneurons.(b)Siliconnano-porespassingvariousbio-moleculessuchasproteins.(c)Capturing?uorescenceactivityofstimulatedneuronsinbrain.(d)Characterizationofcellsmechanicalphenotypesviamicro?uidicchan-

nels...........................................49

vii

4.2Readoutchannelblock-diagram(a)andtimingdiagram(b) 53

4.3Oversamplingoftheintegrationramp.Slopeofthe?tlinerepresentsthenet

inputsignalduringtheintegrationphase 55

4.4SensorICfullchipblockdiagram 56

4.5ThecoreOTAoftheCTIAblock 57

4.6SensorICchipmicrograph(a)andchannelpowerbreakdown(b) 58

4.7Inputreferrednoisespectrumat5kS/sand38MΩoftransimpedancegain.

Slightincreaseat>500HzisduetoTIAloopgaindrop 59

4.8MeasuredINLandDNLoftheoverallchannelusingrampinput.Lessthan100

ppmworstcaseINLandlessthan10ppmofworstcaseDNLaremeasured 59

4.9Measuredspectrumoftonetestwith16nAPPsinewaveinputat8Hz 60

4.10Minimumrequiredsupplycurrentvs.inputreferrednoise?oor.Typicalvalues

areusedfromthedesigntoachieverealistictakeaways 62

4.11Signal?owgraphfora?rstorderDSMcurrentconverter.Thenoisesourcesand

theirtransferfunctionsarehighlightedinred 63

4.12Inputreferredspectrumofa4MS/s1st-order?-Σconverterwithandwithout

theDACnoise.Asseen,theDACnoisecompletelydominatestheinputreferred

noiseofthefrontendatlowerfrequencies 64

4.13Circuitblockdiagramofthenoise-shapingRZI-DAC 66

4.14NS-RZI-DAC200nAoutputcurrenttimedomainwaveform(a)andpowerspec-

trumdensity(b) 67

4.15Signal?owgraphofthemodi?ed?-ΣcurrentconverterusingnoiseshapingRZ

I-DACashighlighted 68

A.1Asimpli?eddiagramoftheZTIA(a)initsnoisesources(b) 75

A.2TheinputreferredstandarddeviationofasinglesampletakeninZTIAandCTIA

schemesversusCPar 77

C.1SimpleCTIAdiagramwithimportantcomponentsshownfortheanalysis 80

C.2TheplotofFoMTIAandFoMSforworkspublishedinISSCCtodate 85

D.1Themagnituderesponseoftheline-?ttingtransferfunctionfora?xintegration

timeofTint=100μs 88

viii

ListofTables

2.1ComparisonwithpriorartsofPPGandSpO2sensors 27

3.1ComparisonwithpriorartsofESI&BioZrecordingICs 47

4.1Summaryofrequirementsforcurrentsensingapplications 52

4.2ComparisonwithpriorartsofcurrentsensingICs 61

ix

TableofAcronyms

Acronym

Description

Acronym

Description

ADC

AnalogtoDigitalConverter

LUT

Look-UpTable

AMB

AmbientEfect

MA

MotionArtifact

CMRR

CommonModeRejectionRatio

MRI

MagneticResonanceImaging

CTDSM

ContinuousTimeDSM

NS

NoiseShaping

DAC

DigitaltoAnalogConverter

OCT

OpticalCoherenceTomography

DAQ

DataAcquisitionUnit

OTA

OperationalTransconductanceAmpli?er

DBE

DigitalBack-End

PAV

PeaksandValleys

DNL

DiferentialNon-Linearity

PCB

PrintedCircuitBoard

DR

DynamicRange

PD

Photodetector(orPhotodiode)

DSM

Delta-SigmaModulator

PM

PhaseMargin

ECG

Electrocardiography

PPG

Photoplethysmography

EEG

Electroencephalography

PSD

PowerSpectralDensity

ESI

ElectrodeSkinInterface

RMS

Root-Mean-Square

FoM

FigureofMerit

RST

Reset

GEVI

GeneticallyEncodedVoltageIndicator

RZ

Return-to-Zero

HR

HeartRate

SAR

SuccessiveApproximationRegister

INL

IntegralNon-Linearity

SFDR

SpuriousFreeDynamicRange

IR

Infra-Red

SNDR

SignaltoNoiseandDistortionRatio

IRN

InputReferredNoise

SNR

SignaltoNoiseRatio

LED

LightEmittingDiode

TFE

TransimpedanceFrontend

LFP

LocalFieldPotentials

THD

TotalHarmonicDistortion

LSB

LeastSigni?cantBit

TIA

TransimpedanceAmpli?er

x

Acknowledgments

Writingthisdissertation,InowwholeheartedlybelievethatstudyingaPhDisajourneythatcomprisesofmanystepsandelementsandisaboutgoingoutsideofone’scomfortzone,spendingalotoftimereadingandlearningaboutthingsthatmayormaynotbehelpfulingettingtothe?naldestinationofaproject,makingcriticalbutwell-informeddecisionsontheroutestotake,communicatingwithpeopleinsideandoutsidethecircleofpeers,dedicatingthetime,potentiallypullingall-nighters,andfailing,andfailingandfailing,to?nallysucceed.Undoubtedly,navigatingthroughsuchajourneyisimpossiblewithoutthehelpofaguide,soI’dliketothankRikkyforallthesupportthroughoutthistime.

I’dliketothankmanyofmycolleaguesandgroup-mateswhogenuinelymadeiteasierformetosurviveandsucceed.

Andofcourseaboveall,Iwouldn’tbeheretodayifitwasnotforthesupportofmydearGoliandmyfamily.Thankyou!

1

Chapter1

Biosensors,GoalsandChallenges

1.1HistoryofBiosensing

Therecordedhistoryofsensingsignalsfromthebodydatesbackto2600BCwhereGilgamesh?rstdescribedandrecognizedthedeathofhisdearfriendbynotbeingabletofeelhisheartbeat,“Itouchhisheartbutitdoesnotbeatatall”hesaid[14].Butasitappears,ittookhumanbeingsmillenniatoimproveonthemethodsandaddtothesignalsandinformationrecordedfromthebody.In1625whenSantorioofVeniceandGalileopublishedtheirbodythermometer,itwasoneofthe?rstattemptstotrytouseanapparatusforrecordingofavitalsign[56].Afterabout80years,Floyer’sreportonmeasuringthetimingofpulsesusingapendulumwaspublishedin1707[71].Thistrendcontinueduntilin1903whenthe?rstEKGmachineswereintroduced[7].Lateronin1950s,electricalactivityofsingleneuronsinbrainofmammalswasmeasuredusingwires[67].Later,theadventofpatch-clampelectrophysiologyin1970sprovidedalotofinsightintothesynaptictransmissionofneurons[60].Inthe1980sto1990s,siliconmicroelectrodearraysbecamethemaintoolininvestigatingthecommunicationofneurongroups[16]andeventodaytheystillformthestandardmethodinrecordingofneuralactivity.

Recordingofsuchsignalshasalwaysbeenaimedtoprovideinformationabouthowcertainsystemsandorgansinthebodyoperate,assistdiagnosisofmanyphysiologicalandneuro-logicaldiseases,enhancetheprognosisofvariousmedicationsandtreatments,aswellastodevelopamorethoroughunderstandingoftheunderlyingmechanismsforvariousillnesses.

1.2BiosensorInterfaces

Anyapparatususedtoacquireabiosignalneedstointerfacewithoneormultiplepartsofthebody.Dependingonthesignalofinterest,therecordingsite,andthesignalquality,thetypeofinterfacecanchange.Thereisawidespectrumofwaysforadevicetointeractwiththehumanbody.FrommechanicalcontacttothebodysuchasinsensingofECGusingpiezoelectricmaterials[23],electricalconnectionsuchasinEKGmachines,optical

2

CHAPTER1.BIOSENSORS,GOALSANDCHALLENGES

DryElectrodes

WetElectrodes

Figure1.1:DrysnapelectrodesfabricatedbyFloridaResearchInstrumentsandgenericwet

electrodesusefulforbiopotentialmeasurements.

interfacesasinpulse-oximeters,magneticandradio-frequencyreadoutsuchasMRI,high-intensityparticlespenetratingthebodyasinX-rayimaging,tothebiochemicalanalysisofbodygasor?uidssuchashumanexhales[15].Thefocusofthisdissertationishoweveronlyonelectricalandopticalinterfacesandthefollowingsectionsreviewthesetwointerfacesmorethoroughly.

ElectricalInterfaces

RecordingofbiopotentialssuchasElectrocardiogram(ECG),Electroencephalogram(EEG),Electromyogram(EMG),andneuralsignalssuchaslocal?eldpotentials(LFP)andspikesrequireselectricalcontactbetweenthesensorandtherecordingsite.Thiselectricalconnec-tionoccursviaconductiveelectrodescontactingtheskin,tissue,ornerveendpoints.Theadvantageofelectricalcontactisthatitprovidesthemostdirectaccesstotherecordingtargetandasaresultitgenerallyachievesahighersignalquality.Fig.1.1showstwosetsofdryandwetelectrodesusefulforbiopotentialrecording.Ontheotherhand,thereareafewchallengesinmakingelectricalcontacttothetargets.Forone,suchacontactre-quiresphysicalaccesstothetargetpointandifthetargetresideswithinthehumanbody,surgicalproceduresfollowedbymaintaininganopenincisionisrequired.Moreover,thefor-eignbodyresponsegenerallydegradestherecordedsignalqualityovertimeandnecessitatesre-implantation.Toaddressthis,wirelesslyoperatingimplantshavebeenproposedintheliteraturetominimizetissuescarringandextendthesensorlifetimeinthebody[20,3].However,thischallengebyitselfisenoughtorestrictelectricalreadouttoonlyapplicationswheretheinformationisotherwiseinaccessiblesuchasrecordingofdeepbrainneuralsignals.Anotherchallengeinusingelectrodesforconnectionistoachievealow-impedancecontact

3

CHAPTER1.BIOSENSORS,GOALSANDCHALLENGES

Figure1.2:AppleWatchheartrateandbloodoxygensensorsonitsbackconsistingoffour

LEDsandfourphotodiodes.

especiallywhencontactingadrysurfacesuchasskin.Thiswillcauseadegradationinthesignalquality,highersusceptibilitytocommonmodenoiseandinterference,aswellasanelevatedinterfacenoiselevel.Chapter3focusesonanambulatoryear-EEGrecordingdevicethatdirectlydealswiththisproblemanddiscussesapotentialsolutiontothischallenge.

OpticalInterfaces

Oneotherapproachincollectingbiosignalsistheuseoflightpropagation,attenuation,orphaseshiftwhenpassingthroughthebiologicalmedium.Opticalapproacheshaverecentlyfoundalotofinterestduetotheirpotentialinprovidingaccesstoinformationwithinrel-ativelydeeperanatomicalareaswithouttheneedforincisionsortissuedisplacement.Fig.

1.2showsAppleWatchseries7opticalsensorsthatcaptureuser’sheartrateandbloodoxygenationlevelusingacirculararrayoffourLEDsandfourphotodiodes.Oneofthemostcommonlyusedopticalmethodsinretrievingbiologicalsignalsisthemeasurementofpulsebyrecordingthetimedomainchangesinthevenoustissuevolumeinresponsetothepul-satileblood?ow.Thetechnique,knownasphotoplethysmographyorPPGinshort,detectsapulsatilecomponentinthetransmittedorre?ectedlightfromthetissuewhoseperiodicitycorrespondstothecardiaccycle.Amajorbene?tofthistechniqueisthatitprovidesoneoftheleastinvasivemethodsofacquiringthepulse.ThetopicofPPGanditssensorswillbediscussedingreaterdetailinchapter2.Thereareplentyofotheropticalapplicationsthatextractinformationfromthebody.Opticalcoherencetomography(OCT)forexampleprovidesmicrometerresolution3Dscansofthehumantissuewithdetailinformationabouteverylayerusingalowcoherencelight.Thistechniquehasbeenusedforreconstructionof

4

CHAPTER1.BIOSENSORS,GOALSANDCHALLENGES

3Dscansofretinalcells[68].Anotherapplicationhastodowithmonitoringofneuralactiv-itiesusingopticalmethods,a?eldknownasoptogenetics[82].Manyindividuallyselectedneuronsaresimultaneouslyexcitedusingaspatiallightmodulatorandtheresponsecanberecordedusinghighsensitivityimagers.Chapter4focusesonarecordingdevicethatcanserveasanimplantableimagertocapture?uorescentactivityofneurons.

Oneofthechallengesofopticalmethodshoweveristhatsincethelightneedstopropagatebackandforthintothetissue,itgenerallyrequiresahigherpowerlevelatthesourcewhenprovidingthedesiredsig