Owl-vision-inspired near sensor computing

Domain: neuroscience
Published: 2026-02-12
Editorial outcome: major_revision

Abstract
Passivetargetdetectioninphoton-starvedenvironmentsiscrucialtoexpandmachinevisioninawiderangeofapplicationssuchasprecisionguidance,intelligentsurveillance,andearlywarning.Here,inspiredbyowlvision,wereportabimodalsynaptictransistorwithanoptoelectronicdecouplingmechanism,enablingparallelphotonicperceptionandelectricalplasticityemulation.Asaresult,thedeviceexhibitsahighactiveadaptationindexofapproximately331,alongsidetheabilitytoperceivelowlightintensitiesasfaintas0.146nWcm-2.Wealsoachievecyclicallystablesynapticweightmodulationwithlong-termenhancementandinhibition,andverifythefeasibilityofweightdeploymentacrossthreebasicartificialneurallevelsoveralightintensityrangeof0.146-11.70nWcm-2,viaadaptivecontrastenhancement.Theowl-vision-inspireddeviceestablishesahardwarefoundationforwardinenergy-efficientandlow-lightimageprocessingforneuromorphicvisionsensors.

Introduction
Thegrowingdemandforartificialintelligencesystemshaveexposedcriticallimitationsinconventionalcomputingparadigms1,2. Whileachievingunprecedentedperformanceincomplexcognitivetasks3,4,thesesystemsgrapplewithunsustainableenergydemands—modernGPUclustersconsumemegawatt-scalepowerduringinference,exceedingbiologicalneuralefficiencybynineordersofmagnitude5. Biologicalnervoussystemsexcelintheirenergy-efficientoperation,particularlythehumanbrain,whichorchestratestrillionsofsynapticoperationspersecondatpowerbudgetsofonly20watts. Thisisachievedthroughhighlyparallelized,adaptive,andanalogprocessingvianeuronsandsynapses6,7.Thesebiologicalmechanisms,includingpulsecoding,synapticplasticity(Fig.1a),anddynamicreconfiguration,serveasinspirationforneuromorphiccomputing,whichaimstoachievesimilarefficiencythroughartificialsynapses(Fig.1b)8,9.Comparedtoconventionalcomputingsystems,thisphysicalneuromorphicarchitecturecanachievebiologically-inspiredperceptionandlearningfunctionsinanefficientandlow-energymanner10,11.

Amongthevariousneuromorphiccomputingapproaches,near-sensorcomputing,whichintegratessensingandprocessingatthepointofsignalcapture,hasgarnered significantattention12–15.Givenvisualinformationaccountsformorethan80%ofhumanperceptualinput,optoelectronicsynapses—devicesthatcombinesignalsensingandprocessing—arecentraltoenablingenergy-efficientcomputation. Comparedwiththe “sensing-processing separation” architectureoftraditionalmachinevision(SupplementaryFig.1),thenear-sensorcomputingsystemshowsanorderofmagnitudeadvantageintermsofenergyefficiency(<1pJ/operation)anddelay16.Despiteprogressinthisfield,thereremainsubstantialchallenges,particularlyinthecontextoflow-lightenvironments,wherecurrenttechnologiesstruggletomatchtheremarkablecapabilitiesofbiologicalsystems17–19. Comparedtolivingcreatures(Fig.1c),imagesensorsbasedoncomplementarymetal-oxidesemiconductor(CMOS)technology(whichisnowmature)canonlyperceivesceneswithanilluminationofabout1.0lux20. Thiscorrespondstoalightdensityofapproximately10−7to10−8Wcm−2. Researchershavetriedvariouspost-processingtechniques,suchasmicrolenses,anti-reflectivecoatings,additionalnear-infraredsensing,noisefilters,andimageenhancementalgorithms,toovercomeultra-lowlightenvironments. However,thesetechniquesoftenhavecompatibilityissueswithmainstreamCMOStechnologyandcomplexsoftwaresystems20,21. Figure1delucidatetheowl’sexceptionalvisualacuityindimlightingfromabiologicalstandpoint. Itsretinacontainsagreaterdensityofrodcellscomparedtohumans,anditsphotosensitivity(P t)canbeenhancedthroughtheaccumulationofphotoninput,aphenomenonreferredtoasdarkadaptationofvision18,22.Uponcompletionoftheadaptation,theimagingofobjectsinlowilluminationontheowl’sretinabecomesmoredistinct23. Therefore,thedevelopmentofanartificialsynapticdevicecapableofmimickingthevisualneuronsofowlsandbeingelectronicallycontrolledtoparticipateinartificialneuralnetwork(ANN)computationspavesthewayforamonolithic,integratednear-sensingvisioncomputingsystem.AsshowninFig.1e,detectionequipmentequippedwiththissystem,suchasunmannedaerialvehicle(UAV),willbeabletoperformlow-energypassivedetectiontasksindarkenvironments,suchasnightskysearchandrescue,deepspaceexploration,andsoon.

Inthisstudy,wepresentanowl-inspireddual-modeadaptivesynapse(ODAS)thathastheadaptivecapabilityofthevisualnerve. Thedeviceisdesignedasathree-terminaltransistorstructurewithaninsulatingpolymerpoly-coatinghavinganesterbaseusedasalight-absorbinglayertoreducethedensityofcharge-trappingstates.Thephotogeneratedcarriersaretransferredtothechannelandaccumulatedusinggatevoltagecontroltodynamicallyenhancethephotosensitivityofthedevice.ODAShasahighactivityadaptiveindexof~331,whichdemonstratesphotonadaptivefunctionalityforsuper-visualsensinginphoton-starvedenvironments(0.146nWcm−2),animprovementofaboutthreeordersofmagnitudecomparedtoCMOSphotosensors. Inaddition,thedevicehaswidespectralsensingenablingpulseweightmodulationfromthenearinfraredtotheultravioletrange(365–940nm).Intheelectricalpulseinputmode,weachievedperiodicstablesynapticweightmodulationoflong-termpotentiation(LTP)withlong-termdepression(LTD)andverifiedthefeasibilityandgeneralizationabilityofdeployingtheweightsinartificialneuralnetworksatthreedifferentspatialscales.UsingUAV,wedevelopedanair-to-groundtargetdetectionneuromorphiccomputingsystemthatmimicstheowl’svisualnervoussystem,withover95%simulationsrecognitionaccuracyonmulti-dimensionaldatasetsthroughadaptiveimprovementofthesimulatedrootmeansquarecontrast.SimulatingneuromorphiccomputingplaysacrucialroleindemonstratingthesuperiorefficiencyofODASdevicesoverconventionalarchitecturesforneuralnetworktasks. Moreimportantly,thesesimulationsprovideessentialguidanceforthedevelopmentofnext-generationdevices,identifyingkeyparametersthatrequireoptimization.ThisparadigmdemonstratesthepotentialapplicationsforwhichODAScanbeusedinadvancedoptoelectronicneuromorphicdevices,pavingthewayforhardwareimplementationofnear-sensorcomputingsystemsinastronomicaldarkenvironments.

Results
ThestructuraldesignofthisopticalsynapsedeviceisshowninFig.2a. TheODASadoptsathree-terminaltransistorstructure,withthegate(G)locatedatthebottomofthedevice.Conductiveindiumtinoxide(ITO)withhightransmittanceisselectedtoachievelight-unobstructedinput.Forthesourceanddrain(S/D),silver(Ag)isinitiallydepositedandsubsequentlymodifiedwithpentafluorobenzothiol(PFBT)toimprovetheworkfunction.Thisapproachallowsapolyvinylalcohol(PVA,SupplementaryFig.2)filmisusedasthedielectriclayer.BecausePVAhasthepropertyoftrappingelectrons,thephotogeneratedelectronsinthehybridlayercanbetrappedonthePVAfilmunderthedrivingofthegateelectricfield,therebyachievingnon-volatileregulationofthecurrent.Thedesignofthelight-absorbinglayeristhekeytoODAS. Leadsulfidenanocrystals(PbSNCs)wereselectedasthephotosensitivematerial,whichcaneffectivelyrespondtolightinputintheinfraredandvisiblelightbands.Inaddition,aninsulatingpolymerpoly(vinyl-cinnamate)(PVCn,SupplementaryFig.2)withanestergroupwasintroducedtoformaconjugatedmixturewithPbSNCstopassivatethechargecapturestate.Thisapproachpreventstheformationofcarriertrapstateswithinthetransportchanneloratthechannel/dielectricinterface.Forthechannelmaterial,thisresultsinanincreaseintherecombinationahigh-mobility2,7-dioctyl[1]-benzothiopheno[3,2-b][1]benzothiophene(C8-BTBT,SupplementaryFig.2)crystallinefilmasthechannelmaterial,ensuringhighsignalamplificationefficiencyofthetransistor.Itslightabsorptioncharacteristicsintheultravioletbandcanextendthedevice’slightdetectionrange.ODAShasanopticalabsorptionlayer,achargetrappinglayerandmultipleheterojunctions,whichcanmaximizetheretentionoftheintrinsicamplificationcharacteristicsofthetransistor,therebyoptimizingthegratingeffect.

InordertodemonstratethattheODAShastheexpectedstructureandproperties,anumberofcharacterizationtoolswereemployedtorevealthematerialproperties. Figure2bdisplaysthetransmissionelectronmicroscopy(TEM)imageoftheODAScross-section,accompaniedbythecorrespondingenergydispersivesystem(EDS)spectra. Thecleancontactinterfaceofthechannellayerwiththesource/drainelectrodeisfacilitatedbythedirectgrowthofC8-BTBTonthepre-depositedelectrode.Thisapproachcircumventsthedamagetothesemiconductor/metalinterfacethatisinherenttoconventionalevaporationandsputteringprocesses.TheaforementionedmethodenablestheC8-BTBTsinglecrystaldomainsizetoreachthemillimeterscale,therebyfacilitatingcoverageofthechannelareaofthedevice(SupplementaryFig.3).Moreover,in-planeX-raydiffractionphi-scanmeasurementonthe(020)planeofC8-BTBTobservedtwosharppeaksatanintervalof180°,furtherconfirmingthesingle-crystaldomainpropertiesofC8-BTBT(SupplementaryFig.4).ThesurfaceofPbSNCs,whichareaphotosensitivematerial,hasahighdensityofelectron-wellstates.Thisresultsinanincreaseintherecombinationprobabilityofphotogeneratedcarriers,whichinturnaffectsthecurrentresponseofthematerialunderlow-lightconditions.ThecombinationofPbSNCswithPVCnservestopassivatetheintra-layertrappingstatesofcarriers,therebyensuringthegenerationofeffectivecarriersunderconditionsofweaklight. ToverifythesuppressioneffectofPVCnonPbSNCtrapstates,PbSNCfilmswerepreparedasacontrolgroupandcomparedwithPVCn:PbSNCblendfilms.X-rayphotoelectronspectroscopy(XPS)inSupplementaryFig.5ademonstratesthatthebindingenergypeaksofPb4finthePVCn:PbSblendfilmareshiftedtolowerenergies,indicatingthattheintroductionofPVCnresultsinareductionofthebindingenergyofPb.Furthermore,theabsorptionspectrainSupplementaryFig.5brevealsignificantlyenhancementreflectsimprovedphotogeneratedcarrierefficiencywithinthematerial,typicallyassociatedwithreduceddefectstates.Asnon-radiativerecombinationcenters,trapstatesquenchphotogeneratedcarriers,therebydiminishingeffectiveabsorption. TheaboveresultsdonotcontradictthehypothesisthattheintroductionofPVCnmaycontributetothepassivationoftrapstatesinPbSNCs,yetdirectevidencesupportingthisspecificpassivationeffectremainscurrentlymissing.AvisualinspectionoftheSTEMimageinSupplementaryFig.6indicatesthatthePbandCelementsaredistributedinauniformmanneracrossthePVCn:PbSblendfilm.Atomicforcemicroscopy(AFM,SupplementaryFig.7)providedfurtherconfirmationofthefilm’shomogeneity.Inaddition,spectralabsorptiontestswereperformedonPVCn:PbSNCblendfilmsandC8-BTBTcrystalfilmstoillustratethattheyhavedesirablephotosensitiveproperties. Theabsorptionspectra(Fig.2c)showthatthePVCn:PbSNCblendfilmhaseffectiveabsorptioninthevisibleandinfraredbands,andtheC8-BTBTcrystallinefilmhasastrongabsorptionphenomenonforUVlightbetween300–400nm.Thetrendoftheabsorptionspectraisinagreementwiththeresultsofthepreviousreport24.AlthoughC8-BTBTexhibitsphotoresponsivepropertiesintheultravioletband,intheactualoperationofODAS,lightstimulationisinputviathegate.Underweaklightconditions,short-wavelengthlightstrugglestopenetratethePVCn:PbSlayerandparticipateintheexcitationofchargecarriersinC8-BTBT.Tovalidatethishypothesis,weconductedcomparativeexperiments(supplementaryFig.8).TwosetsofdeviceswiththesamePVCnthicknesswereprepared.OnegroupwasdopedwithPbSquantumdotsastheexperimentalgroup,whiletheothergroupconsistedofpurePVCnthinfilmsasthecontrolgroup.TheUVlightresponseofthedeviceswastestedunderbothconditions.TheresultsshowedthatthedevicesexhibitedsignificantphotocurrentresponseonlyunderthePVCn:PbSthinfilm,furtherconfirmingthatthePVCn:PbSlayeristheprimarysourceoflightabsorptionandphotogeneratedcarriers.

ODASbenefitsfromtheaccumulationofcarriersinthechannelduringthelightadaptationprocess,whichgraduallyincreasesthephotosensitivity. Figure2dillustratesthismechanism.Inabsolutedarkness,thedeviceisinanon-adaptedstate,withalmostnoelectron–holepairsinthePbSlayer,andtheconductivityoftheC8-BTBTlayerismainlydeterminedbytheconcentrationofintrinsiccarriers. Whenthedeviceisinaphoton-starvedenvironments,photonsinthePbSlayercaptureenergyandgenerateelectron–holepairs.Astheirradiationcontinuestoextendandanexternalgatevoltageisadded,theelectron-holepairsseparate,withtheelectronsbeingcapturedbytheactivefunctionalgroupsinthelowerdielectriclayerandtheholesaccumulatinginthedevicechannelthroughthegatingeffect,resultinginanenhancementofthechannelcurrent,animprovementinthephotosensitivityandultimatelyreachinganequilibrium.Afterthelightisremoved,someofthetrappedelectronswithshallowenergylevelsarereleasedtorecombinewiththeholes,butmostoftheelectronsremaintrappedduetotheenergylevelbarrier,maintainingthephotosensitivityofthedevice.Thisisthereasonforthenon-volatilenatureofpersistentphotocurrent(PPC).TheenergybandstructureofthismechanismisillustratedinSupplementaryFig.9.ToverifythelightadaptationcharacteristicsofODAS,theIDSwastestedataspecificlowlightintensityasafunctionoftheadaptationtime,andtheresultsaredemonstratedinFig.2e.Evenatanultra-lowlightintensityof0.146nWcm−2,theadaptationcurrentofODASstillincreasesandaccumulateswithinatimescaleofdozensofseconds(consistentwithbiologicalorganisms),demonstratingsignificantadaptationeffects.Furthermore,basedonthelightsensitivitycharacteristicsoftheowlretina,whicharecharacterizedbyanincreaseinthepostsynapticpotentialofrodcells,thelightsensitivityofthisdevicecanbequantifiedasP t,calculatedasfollows:

P t= I t (cid:1) I dark / I dark (1)

whereIdarkisthedarkcurrentandItisthechannelcurrentofthedeviceaftertsecondsofadaptation22.Figure2fshowstheproportionalrelationshipbetweenP tandilluminancefordifferentadaptationtimes.Inthebiologicalvisualadaptationsystem,activeadaptationisusuallydescribedbythechangeinsensitivityduringexposure.Here,thetime-dependentactiveadaptationindex(AAI)isdefinedasthechangeinP tasafunctionoftheorderofmagnitudechangeinilluminance,definedasfollows:

AAI= dP t / dlogL (2)

whereLis theilluminancevalue.ItisgenerallyacceptedthatP tisindependentofLwhenAAIislessthan1.0[22].AAIofODASwithluminancefordifferentadaptationtimesisgiveninFig.2g.Whentheluminanceisof≤0.73nWcm-2andtheadaptationtimeis5s,theAAIstaysbelow1.0,andthesensitivitygrowthisnotactivated.Whentheadaptationtimewasmorethan10s,theAAIremainedhighlevelatallilluminances,andP twaspositivelycorrelatedwithL.Whentheexposuretimewas25s,AAIreachedamaximumvalueof331atalightintensityof0.146nWcm-2.ThisfurtherprovesthatODASstillhaspositivephotoadaptationinlow-lightenvironments.Sincelightsignalsintherealworldhavedynamiccomplexity,SupplementaryFig.10showsadditionaltestsfordynamicadaptationunderconditionswherelightinputchangesovertime,verifyingtheresponsecapabilityofODASinnon-idealdynamicenvironments.

Reliabilityofsynapsesarray

Tofurthersimulatethedynamicadaptabilityoftheowl’sretinatolowilluminationlevelsandtoverifythereliabilityofODASinfuturepracticalapplications,weconstructeda19×17ODASactivearray.Thearrayingofsynapticdevicesisnecessaryfortheweightcalculationoftheneuralnetworkintheinferencestage.AsshowninFig.3a,thearrayarchitectureconsistingofsynaptictransistorscanbeusedforweightdeploymentinavarietyofinferencemodelssuchasmultilayerperceptron(MLP),convolutionalneuralnetwork(CNN),anddeepneuralnetwork(DNN).OpticalphotographsoftheODASarrayaredemonstratedinFig.3b(themanufacturingmethodofthesynapsesarrayreferstothemethodssectionandSupplementaryFig.11).ThearrayisopticallytransparentduetothesubstrateofglasswithITOparallelelectrodestrips.TheequivalentcircuitoftheactivearrayisshowninFig.3c.Bitline,wordlineanddrainvoltageconstructthecross-arrayarchitecture. SynapticconductancemodulationintheODASisachievedbyapplyingvoltagesignalstothearraythroughthewordline.AsshowninFig.3dandSupplementaryFig.12,weselectedthe17×17portionoftheartificialsynapsearray(excludingtheboundaries)fordevice-to-devicebidirectionalIDS-VGscanningtestsandIDS-VDtests.ODASexhibitstypicalp-typetransistorcharacteristics,withaVTHofapproximately1.5V.Thecurvedemonstratesacounter-clockwisehysteresisunderbidirectionalscanning,indicatingthat“thedevicepossessesstoragecapability.Subthresholdswing(SS)oftheODASis~200mVdec−1,exhibitingefficientelectrostatictuningandexcellentamplificationcharacteristics. Figure3eshowsthespatialdistributionofVTHinthearraywitharootmeansquarederrorof~0.09V,provinghighuniformityofthedevicearray.Thisisfurthersupportedbythestatisticaldistributionoftheon/offratio(Fig.3f). Additionally,theconductivityretentioncharacteristicsandcyclicstabilitytestresultsofODASareshowninSupplementaryFig.13. Theconductivityofthedevicedidnotshowsignificantdecaywithin104safterlightstimulation,andnosignificantdegradationofODASperformancewasobservedduring10⁴IDS-VGcyclicscans.Morenotably,withintheoperatingrangeof293.15–353.15Kand30-80%humidity,thedevice’stransmissioncharacteristicsremainedstable,withnoobserveddegradationoftheconductivestateorsystematicthresholddrift(SupplementaryFig.14).Thisprovidesdirectevidenceofthedevice’sengineeringfeasibilityundermulti-environmentalconditions.

Afterconfirmingtheconsistencyofthesynapsearray,inordertogeneratearealisticadaptationandimagingsceneunderlowlightconditions,anopticalpatternconsistingofthefiveletters,“X”,“J”,“T”,“L”,and“U”wasprojectedontothedevicearray(lightintensity=0.73nWcm−2). Figure3gvisualizesthechangeinthenormalizedphotocurrentoftheprojecteddeviceduringthe20-secondadaptationtime. Astheadaptationtimeincreases,imagedetailsaregraduallydistinguishedduetothedifferencesinilluminationonthepatternsurface.Thisprocessrevealsthevisualadaptationandimagingoftargetsbytheowl’sretinaunderultra-lowilluminanceconditions,andverifiesthefeasibilityoftheODASarrayinfuturemachinevision.

Photo-controlledsynapticproperties

Intheowl’svisualsystem,lightpassesthroughthecorneaandlensoftheeyeandfocusesontheretina,wherethesynapticstructuresofthephotoreceptorcellsareresponsibleforconvertingandinitiallyencodingthelightsignal.Whenphotonsreachthephotoreceptorcells,theopsinproteintriggersaseriesofbiochemicalreactionsthatcausethesynapticsodiumchannelstoclose,therebyreducingtheentryofsodiumionsintothecell.Thiscausesthecellmembranetodepolarize,whichinturngeneratesanactionpotential25.Thehighlysensitiverodcellsintheowl’sretinagiveitexcellentnightvision,enablingittoaccuratelyidentifytargetsevenin<0.01lux19,26.What’smore,owlswithspecialconecellscanperceiveawiderrangeoflight(fromnear-infraredtoultraviolet)thanhumans.ODAS’photoreceptorcharacterizationmethoduseslightpulsesasinput.Figure4adisplaysthespecificteststrategy.Alightpulseofaspecificenergyisinputintothetransparentgateofthedeviceusingalight-emittingdiodeconnectedtoasignalgeneratorasthelightsource.Atthesametime,avoltageVDSisappliedtothesource-draintomonitorthecurrentandobservethenon-volatileresponsetotheODAStotheopticalinput. Figure4bdescribestheexpectedinput/outputschematic.Thesynapseiscontinuouslystimulatedwithlightintheformofidenticalpulses,andthecurrentoutputshowsastepchange.Thepulseinformationismemorized.Intheactuallightpulsetests,fivewavelengthsoflight-emittingdiodeswereemployedbasedontheowl’swavelengthperceptionrange:365nm,470nm,590nm,and940nm.Theoutputlevelofthesignalgeneratorwasadjustedusingalightintensitymeterplacedinthetestenvironmentsothatthefivediodesemittedlightpulsesofthesameintensity.Figure4cshowstheODASresponsetofourwavelengthsoflightstimuli(1.46nWcm−2)atVDS=−1V,withapositivebiasvoltageof0.5V(SupplementaryFig.15illustratesthatODAShasthehighestP tata biasvoltageof0.5V). Thetestresultsindicatethatthedevicehasthesamenon-volatilepersistentphotocurrent(NV-PPC)trendforthefourwavelengthsof365nm-940nm,andthecurrentgainincreaseswiththedecreaseofthelightwavelength.Thisresultisconsistentwiththeexpectedstepwisecurrentchange.ThisindicatesthattheODASiscapableofreceivingawiderangeofopticalsignalsfromultraviolettovisibletoinfraredtosupportitssynapticperformance.Inaddition,theintensitydependenceoftheODASasanopticalsynapseforpulsedsignalswasalsotested(Fig.4d).ThedeviceshowedapositivelycorrelatedPPCchangefordifferentintensities(0.73nWcm−2,1.46nWcm−2,and4.38nWcm−2)ofopticalpulseinputs.Theattenuationrates(≤1.3%)afterstimulationwithlightpulsesofthreeintensitieswerecalculatedinFig.4e,demonstratingthelong-termopticalinformationmemoryproperty. SupplementaryFig.16alsosupplementstheODASpulsewidthdependenceandnumberdependencetests,whichprovesthatODAShasarichabilitytomodulateopticalsignals.

Furthermore,thelimitingopticalintensityforODAStoperceivelightsignalswasinvestigated.Weuseda940nmlightsourcetostimulatethedevice.Figure4fshowsthattheODASretainsasignificantnon-volatilecurrentgainevenatalightintensityof0.146nWcm−2. Thisresultconfirmsthatthedevicecancaptureweakphotons.Figure4gprovidesacomparisonofODASwithotheroptoelectronicsynapticdevicesthathavebeenreportedinrecentyears17,25,27–37.Theverticalaxisrepresentstheenergyconsumptionofthedevicefordetectinganopticalpulse,whilethehorizontalaxisindicatesthelightintensityforopticaldetection.ODASachievesalowlightdetectioncapabilityat0.146nWcm−2withalowenergyconsumptionof0.4nJperoperation.SupplementaryTable1providesdetailedparametersofthecitedliterature.Finally,the“re-memory”abilityofODASwasdemonstrated(Fig.4h).Duringthetest,19nAwasspecifiedasthetargetforsynapticweightregulation(normalizedto1.0).Toreachthetargetduringtheinitialperception,21inputlightpulseswererequired.Duringthesecondperception,thenumberofinputpulseswasreduced,andonly9pulseswereneededtoreachthetarget.Inthethirdtrial,just6pulseswerenecessary.Theabilityto“re-memory”isakeyattributeofsynapticdevicesforartificialintelligenceapplications,asitemphasizesthebiologicallydynamicoptimizationofsynapticweights38,39.

Electricallycontrolledsynapticproperties

Synapseslocatedinthecentralnervoussystemoflivingorganismsenabletheexchangeofinformationbetweennervecellsmainlythroughthetransmissionofelectricalsignals.Similartothenervoussystem,electricalpulsesareusedasinputsignalsintheworkingofthecomputationalmodulesinthephysicalneuralcomputingarchitecture40,41.Theplasticmodulationofthepostsynapticcurrentbythepulseinputisoneofthenecessaryfunctionsofanartificialsynapse.InthepreparedODASdevice,thegateelectrodeandthesemiconductorchannellayerrepresentthepre-synapsesandpostsynapse,respectively.Theexcitatorypostsynapticcurrent(EPSC)respondstothechannelconductance,whichrepresentsthemodulationofthepost-synapticsignalbythepre-synapticsignal.Figure5ashowsaschematicdiagramofthesynaptictestwithagatevoltagepulseastheinput.Accordingtobandengineeringanalysis,whenapositivegatevoltageisapplied,thegateelectricfieldinduceselectrontunnelingfromthePVCn:PbSlayertothedeepdefectstatesofthePVAelectroncapturelayer.Thisprocessleadstotwosynergisticeffects:first,theconcentrationofholesintheC8-BTBTchannelrises,andtheconductivityincreases;second,theelectronscapturedbyPVAformlocalizedpotentialtraps,inhibitingcarrierrecombinationandachievinglong-lastingretentionoftheconductivestate.Whenanegativegatevoltageisapplied,theelectronstrappedinthePVAlayerescapetothePVCn:PbSlayerthroughemission,whilethegateelectricfieldrepelstheholesintheC8-BTBT,resultinginadecreaseinthenon-volatileconductivity.

AsshowninFig.5b,whenapositiveelectricalpulse(Vbase=0.2V,Vpeak=1.2V,width=500ms)isappliedtoODAS,thereisannon-volatileincreaseinEPSC,whichisconsistentwiththebehavioraltrendofsynapticLTP.Inaddition,whenvoltagepulsesofdifferentamplitudes(1.0V,1.5V,2.0V)areapplied,theEPSCincreaseisdifferent(Fig.5c),whichindicatesthatODAShasamplitude-dependentelectricalpulseconflictplasticity. SupplementaryFig.17furtherdemonstratesthatODASelectricalpulseconflictplasticityisalsorelatedtopulsewidth,frequency,andnumber.IncontrasttoLTP,LTDisregulatedbyapplyingnegativegatevoltagetothedevice.Figure5dshowsthatafter16positivepulses(Vpeak=1.2V,Vbase=0.2V),thesamenumberofnegativepulses(Vpeak=−0.2V,Vbase=0.2V)areappliedimmediatelyafterwards.ThePSClevelreturnstotheinitialstate,completinganLTP-LTDcycle.After30consecutivecyclesofthistest,nosignificantdegradationindeviceconductanceisobserved.Figure5egivesastatisticalanalysisofthePSCscorrespondingtothehighestandlowestconductancestatesduringthese30cycles.ThemedianofhighPSCvalueremainsat32–34nA,andthemedianoflowPSCvalueremainsat13–16nA,revealingthecyclicstabilityofODASintheregulationofsynapticweightbyelectricalcontrol. Additionally,thesaturationcapacityandmaximumoperatingfrequencyoftheODASintermediateconductivestateweretested.Afterapplying128positiveandnegativepulses(7bits)totheODASinsequence(SupplementaryFig.18),thedeviceexhibitedaricherintermediatestate,enablingpreciseneuralnetworkregulation. Furthermore,theoperationfrequencyofupto10HzsupportstheupdaterateoftheODASinactualweightdeployment(SupplementaryFig.19).InadditiontothevalidationofthepulseplasticityoftheODAS,theenergyconsumptionofthedeviceinpracticaloperationwasalsotested,whichprovidesconfirmingitslowenergyconsumptioncharacteristicsoftheODASforfutureuseinneuromorphiccomputingarchitectures.AsshowninSupplementaryFig.20,atverylowVDSandelectricalpulsestimulation,theODASwasstillabletotriggeranEPSCresponsewiththeenergyconsumptioncalculatedas:

E=VDS × Ipeak × t (3)

whereVDS=−0.1mVisthevoltageappliedtothedrainterminalofthesynaptictransistor,Ipeak=−9.2nAisthespikecurrentflowingfromthedraintothesourceterminal,andt=10msisthewidthoftheelectricalpulse.Thus,theminimumconsumptionofODASpersynapticeventis9.2fJ,whichislowerthantheenergyconsumptionofbiologicalsynapses(~10fJpersynapticevent)25.

ToverifythefeasibilityofdeployingODASinelectroniccontrolforartificialneuralnetworktasks,anactualnetworkmodelwasbuilttoperformtheimageclassificationtask. Figure5fshowsthemechanismofnetworkweightdeploymentatfirst.AsthemostimportantpartoftheANNcalculation,theweightmatrix(Wm×n)storestheweightvaluesbetweennetworknodes.Duringtheforwardpropagationprocess,theweightmatrixismultipliedbytheinputmatrixtoobtaintheinferenceresult,andduringthebackpropagationprocess,thenetworkperformanceisdynamicallyupdatedandoptimized.Intraditionalcomputerarchitecture,aweightvalueusuallyconsistsofafloating-pointnumber,occupiesa32-bitstoragespace,andcanberegardedasacontinuousvalue.However,theweightvaluerepresentedbyanODASartificialsynapseis4bits(16conductancestates),whichcanberegardedasdiscrete.Althoughdiscreteweightswillinevitablyleadtoadecreaseinaccuracyinneuralnetworkcalculations,theyhaveabsoluteadvantagesintermsofsavingphysicalspace,reducingcomputationalenergyconsumption,andimprovingcalculationspeed. Figure5gdemonstratestheclassificationresultsoftheMNISThandwrittendigitdatasetafterdeployingthe4-bitconductivestatespaceofODASinMLP(SupplementaryTable2)network. Theclassificationaccuracyis>90%onboththetrainingsetandthetestset,andthenetworklossvaluetendstoconverge.ThisdemonstratesthefeasibilityofintegratingODASintobasicneuralnetworks.TofurtherverifythegeneralizationabilityofODASweighttuning,wetrainedthreedatasets(MNIST,Fashion-MNIST,andCIFAR-10,SupplementaryFig.21)inMLP,CNN(SupplementaryTable3),andadeeplearningnetwork(VGG16),respectively. SupplementaryTable4andSupplementaryFigs.22–24summarizetheclassificationaccuracycomparisonofODAS4-bitconductivestateand7-bitconductivestateparticipatingintheabovetasksimulations.Thisvalidatesthehighnetworkgeneralizationabilityofthesynapticdevice,whichcanintegratenetworkmodelsofmultiplecomplexitiesandperformdiverseimageclassificationtasks.

Targetrecognitioninphoton-starvedenvironments

Thisstudyconstructsanair-to-groundtargetrecognitionsystembasedonthebioniclightadaptationmechanism.Thenear-sensorcomputingsystemcomposedofODASissimulatedbyadrone,whichsimulatesthephotoadaptationofowlvisionandthecentralnervoussystem’simageprocessingcapabilitiesinanightenvironment(Fig.6a). Theexperimentwasconductedinafive-levelgradientbrightnessscene,withalightintensityrangeof0.146–11.7nWcm−2. Alow-resolutiongarmentwasusedastherecognitiontarget(Fig.6b).SupplementaryFig.25andFig.26showtheUAVplatformusedfordatasetcollectionwithpartoftheobtainedair-to-groundimages.Thedatasetfeaturesmultipledimensions:varyingangles,backgrounds(turf,riverside,andmudflat),andheights(30-100m).Theimagesindifferentbrightnessenvironmentsarepreprocessedbysimulatingtherelationshipbetweenrootmeansquare(RMS)contrast,definedatSupplementaryNote1,andadaptationtimeshowninFig.6c.Whentheambientlightintensityis0.146nWcm−2,thesystemincreasestheimageRMScontrastfrom0to21within20s.Thisprocessconstructsanonlinearconversionmodelofphotocurrent-contrastbymappinggrayscalevaluestonormalizedphotocurrents(0–255range),providingatrainingdatasetfortheneuralnetworkmodeltoadapttolowsignal-to-noise-ratioconditions(refertoSupplementaryFig.27forfeaturedistribution).Forthecomputationalmoduleinthenear-infraredreceptorcomputingarchitecture,YOLOv5_sisusedastheobjecttobedeployedforthesynapticmatrix42(SupplementaryFig.28),andthereadouttimeforthisarchitecture’ssingle-layersynapsewasinvestigated(SupplementaryNote2).Theresultsofnetworktraining(Fig.6d)showthatduringtraining,thelossfunctionconvergesfasterwithincreasingbrightness.Forthe11.7nWcm-2lightadaptationdataset,itconvergestobelow0.2after80epochsduringtraining,whileforthe0.146nWcm-2lightadaptationdataset,ittakes180epochstoconvergetothesamelevel.TheperformanceevaluationoftheaccuracyisdisplayedinFig.6eandSupplementaryFig.29.Asthetrainingcycleincreases,mAP_0.5improvesfromtheinitialvalueof0.62to0.98,mAP_0.5:0.95isoptimizedfrom0.31to0.89,andthemaximumprecisiongreaterthan95%,whichverifiestheenhancementofthelightadaptationmechanismonmulti-scaleobjectdetection. Furtheranalysisshowsthatthesystem’srecognitionconfidenceisrelatedtotheadaptationtimeofthedataset(Fig.6f).Whenthelightintensityis0.73nWcm−2,thenetwork’sconfidenceintargetgraspingincreasesfrom0to0.85duringanadaptationtimeof20s.Figure6gandSupplementaryTable5comparetheperformanceofODASwiththatoftherecentlyreportedartificialopticalsynapsesinaneuralnetworkrecognitionsimulation43–50. Thehigherlevelofrecognitionaccuracy(95.2%)ofourdeviceunderamuchweakerlightstimulus(0.146nWcm−2)demonstratesthattheODAS-basedsimulationsystemhastheabilitytorecognizetargetsindarkenvironments.Thisresultprovidesahardware-algorithmcollaborativeverificationframeworkfortheapplicationofbiomimeticmachinevisioninfieldssuchasnightsearchandrescueanddeepspaceexploration.

Methods
Material

AnhydroustoluenewasacquiredfromShanghaiAdamasReagentCo.,Ltd. Polyvinylalcohol(withanaveragemolecularweightofabout31kDa)waspurchasedfromShanghaiAladdinBiochemicalTechnologyCo.,Ltd.Chlorobenzene(99.5%)wassourcedfromMacklin,whilebutylamine(99%)camefromTCIChemicals.Polyvinylchloride(PVCn)withanaveragemolecularweightofapproximately40kDa,polystyrene(PS)at200kDa,andPFBTweresourcedfromSigma-AldrichCo.Additionally,thecompoundC8-BTBTwasprovidedbyLuminescenceTechnologyCorp.(Lumtec).Allmaterialsandsolventswereusedasreceivedwithoutfurtherpurification.

Fabricationofthefilms

TopreparetheC8-BTBTcrystallinefilm,10mgofC8-BTBTand10mgofpolystyrene(PS)weredissolvedin1mLoftoluene.Theresultingsolutionswerecombinedata1:1volumeratioandsubjectedtoultrasonicationforabout2mintocreatetheC8-BTBTsolution.ForTheelectrodemodificationsolution,25μLofPFBTwasmixedwith25mLofisopropanoltoproducethePFBTsolution.C8-BTBTcrystallinefilmswerefabricatedonthesurfaceofasubstratecoveredwithsource/drainelectrodesusingbladecoatinginambientairatroomtemperature.AblendedsolutionofC8-BTBT:PSwasinjectedintothegapbetweenthesubstrateandtheblade.Itwasthenmovedataconsistentspeedof200μms-1usingasteppermotor.Asthesolventgraduallyevaporatedatthefrontendofthemeniscus,thecrystalsgrewcontinuously,resultinginalargeanduniformC8-BTBTcrystallinefilm.Thecrystallinityofthethinfilmswasanalyzedusingacrossed-Nicolspolarizedmicroscope(OlympusBX53).In-planeXRDmeasurementswereperformedwithathin-filmdiffractometer(BrukerD8DISCOVER)utilizingmonochromatedCuKradiation.Thediffractionintensitywasrecordedwithascintillationcounterundernormalconditions,withagrazingincidentangleof0.20°setforthein-planeXRDmeasurement.

TopreparethePVCn:PbSNCblendfilm,25mgmL−1ofPbSNCsolutionwasaddedto60mgmL−1ofPVCnsolutionandstirredforoneminutetoobtainamixedPVCn:PbSNCsolution.Theobtainedsolutionwasspin-coatedat500rpmfor5sandthenat3500rpmfor60s;thesecondstepofspin-coatingfor5swasfollowedbyvacuumannealingat100°Cfor1htopreparea400nmthickPVCn:PbSNCblendfilm.ThefilmswerethenexposedtoUVlightfor1hforUVcrosslinking.Filmsurfacemorphologyandthicknessweremeasuredbyanatomicforcemicroscope(AsylumResearchCypherS).ForXPSmeasurements,thegrownhybridfilmsweresealedinavial,placedinagloveboxfilledwithN2,andthenquicklytransferredtoanXPSvacuumchamber.TheXPSstudieswereperformedusingaKratosAxisUltraDLDspectrometerequippedwithapanchromaticAlKaX-raysource.ThespectrometerwasequippedwithapanchromaticAlKaX-raysource.operatingat150Wandavacuumpressureof10−9mbar.

Fabricationofthedevice

ThesubstratewasfirstpreparedbycleaningtheITO-coatedglassusingaUVozonecleanerfor20min.15mgofPVAwasaddedtoamixtureofwater-ethanolratioof1:1forthepreparationofPVAsolution,whichwasthenspin-coatedontothesubstrate(ataspeedof5000rmin−1),atwhichpointthethicknessofthePVAfilmwasequaltoapproximately50nm.Thefilmwasthenannealedforonehourat100°Conaheatingtable.PVCn:PbSNCblendfilmswerepreparedonthePVAsurfaceasdescribed.Afterdrying,Agelectrodeswerepreparedontheirsurfacebythermalevaporation(depositionratewasabout0.4Ås−1)andAgvaporsweredepositedasS/Delectrodesunderthemaskplate.Then,C8-BTBTcrystalfilmswerepreparedonthesubstrateasdescribedabove.InordertoimprovetheworkfunctionoftheAgelectrodeandenhancetheholeinjectionefficiency,thesubstratewasimmersedinPFBTsolutionfor5mininadvanceandtheAgsurfacewasmodifiedwithPFBTatroomtemperature.Cross-sectionalTEMsamplesofthedevicewerepreparedbyastandardfocusedionbeamboostingprocessonaFEIquantum3Ddual-beammicroscope.TEMimagesofthedevicewereobtainedonaFEITalos200Xmicroscopewithanaccelerationvoltageof200kV.Further,regardingthepreparationmethodofa19×17ODASarray,ITOparallelelectrodestripswerefirstlyemployedasgateelectrodes(spacing=200μm).ThenPVAandPVCn:PbSNCblendfilmwassequentiallydepositedontothegateelectrodearrayasdescribedabove.Inthisprocess,aportionofITOwasexposedbyplacingametalbarlayerontheedgeoftheITOelectrodestrips.ThenS/Delectrodeswerepreciselydepositedonthetopofthegateelectrodesbyapatternedmaskplate.AfterperformingthePFBTtreatmentprocess,athinfilmofC8-BTBTisdepositedontheS/Delectrodepatternwhilethemaskisremoved.TheODASdevicearrayfabricationiscompleted.

Electricalmeasurements

Asourcemeter(KeysightB1500A)coupledtoaprobetablesetupwasusedtoevaluatetheperformanceofODASsingledevicesversussynapticarraysintermsofphotoelectricalsynapticproperties.Themeasurementswereperformedinaclosedchamberatroomtemperature.Thechamberwasshieldedfromexternallightorradiationinterferenceundernormalatmosphericconditionsandgroundedtoavoidelectro-magneticinterference(EMI).Monochromaticlightemittingdiodes(LEDs)offourwavelengths(365nm,470nm,590nm,and940nm)areutilizedtogeneratethelightstimulus.Thepulseinputwascontrolledbyasignalpulsegeneratorunit(SPGU,KeysightB1525)mountedonthelightsourceinstrument.Lightintensitywasmeasuredbyusingacalibratedsiliconphotodetector(Newport843Rwithan818UV/DBopticalpowerdetector).

Simulationofimagerecognition

Tosimulatethevisualadaptationintheowlretina,wefirstdefinedthefunctionforthecontrastofimaging(Ct)obtainedfromthelightintensity-dependentandtime-dependentpropertiesoftheODAS23:

Ct=255×(It-Idark)/(Is-Idark) (4)

whereItisthecurrentafteradaptationfortimet.Idarkisthedarkcurrent,definedhereas10pA,correspondingtograylevel0.Isisthesaturationcurrent,definedas2nA,correspondingtograylevel255.CtobtainedunderdifferentlightintensitiesisthenusedastheRMScontrastindexofthepreprocessedimagetoobtainthesimulatedimageofODASperceptionandadaptation.Theneuralnetworksimulationisrealizedthroughawell-establishedsynapticcomputingframework51,52.NonlinearityofLTPandLTD,definedatSupplementaryNote3,wasfirstevaluatedusingtheNeuroSim+frameworkatafixednumberofappliedstimuli53. Subsequentdeploymentofweightmatricesineachmodelwasperformedusingnormalizedconductancevalues(G),whichwascalculatedasfollows:G=(Gn-Gmin)/(Gmax-Gmin),whereGnistheconductancevalueofthenthpulse,Gmaxisthemaximumconductancevalue,andGministheminimumconductancevalue.Duetotheneedtodynamicallytrackmodelperformancechanges,thedeploymentofweightsandevaluationofthesimulatedphysicalmodelwillbeperformedaftereachepochtraining.

Dataavailability

AlldatasupportingthisstudyanditsfindingsareavailablewithinthearticleanditsSupplementaryInformation.Thedatacorrespondingtothisstudyareavailablefromthefirstauthorandthecorrespondingauthorsuponrequest.Sourcedataareprovidedinthispaper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Authors have made substantial revisions and addressed most of my concerns. However, there are still several issues to be addressed as follows:\n1. I cannot find responses to my previous Comments 1 and 2.\n2. The response to Comment 7 remains unsatisfactory. The authors claimed that “the readout time scale in actual system operation falls within the millisecond-to-second range”. However, given the large scale of a neural network (e.g., YOLOv5) and the limited number of ADCs/DACs, fast readout of even one layer of synapses is impossible within the characteristic time constants of the device. Otherwise, the authors should provide a time estimate or evidence for the training time of one layer. Moreover, no overlapping of the device’s state levels should occur within this time scale.\nRegarding Reviewer #2's comments:\nThe main concerns in Point #1 and Point #2 center on the lack of control experiments and supporting data. It might be feasible for the authors to provide these if the manuscript proceeds to the next revision round.\nPoint #3 and Point #4 relate to the work's novelty and achievements. The papers cited by Reviewer #2 focus on the topic of \"weak-light detection,\" whereas this manuscript combines \"weak-light detection\" and \"neuromorphic electronics\". This was conceptualized as \"owl-vision-inspired near-sensor computing\" by authors. In my view, this concept holds novelty and would be appealing to a broader audience. Thus, I suggest that the authors focus more on highlighting this concept (rather than overemphasizing performance) and provide solid evidence to support it.\nHowever, as I noted earlier, the work contains somewhat exaggerated descriptions (a concern also raised by Reviewer #2 in Point #4). For instance, both the response time (which I previously pointed out) and the detection limit are not competitive when compared with existing studies. The detection limit, in particular, is questionable: To claim a low detection value of 0.001 lux, the authors used a 470 nm light source in their SI, yet a 940 nm light source was predominantly used in the main text. Importantly, 470 nm is a high-energy photon source, which can elicit a strong device response even at low light intensity. Even if the 0.001 lux detection limit is taken at face value, the performance still does not match that of prior works.\nMore critically, there is a confusion between units: \"lux\" measures illuminance, while \"W/m²\" measures light intensity. The description in Figure 4: \"under ultra-low light intensity of 0.001 lux at 940 nm\", is therefore incorrect, as the conversion factor from light intensity to illuminance (in lux) is zero for a 940 nm wavelength. If possible, I would like to add an additional comment to my review: The authors have misused the terms \"light intensity\" and \"illuminance\" throughout the manuscript (e.g., in the caption of Figure 4f). They should provide detailed calculations or definitions to clarify how the device's illuminance detection limit was measured, and provide a comparison to reported works.", "reviewer_2": "This manuscript proposes a device capable of sensing down to 0.001 lux, utilizing non-volatile optoelectronic responses from dielectric defects for deploying neural network weights and enabling ultra-low-light target recognition. While the authors claimed to address previous reviewing concerns in their response letter, the revised manuscript still lacks sufficient data support. Furthermore, the device design is of insufficient innovation, experimental data are incomplete, the core mechanism is inadequately verified, and the applications are unoriginal.\nTherefore, this revision does not meet the standards of Nature Communications and is recommended for submission to more specific journals.\n1. For the device structure Ddesign: the authors repeatedly claim that the incorporation of PVCn reduces electronic trap states in PbS nanocrystals. However, there are no controlled experiments testing different PVCn dosages or comparative analyses of relevant device performance. The authors' response on Page 18 of their response letter lacks specificity and is unsupported by relevant experimental data. Furthermore, the authors state that \"pure PbS quantum dot films cannot form stable functional channels\" as justification for not providing control devices without PVCn. This omission makes it impossible to determine whether the enhanced optoelectronic performance results from improved device morphology or genuine defect passivation.\n2. For the device mechanism: the experimental evidence provided for the operating mechanism are still very insufficient. No transient spectral data, characterization data, or theoretical calculations are provided to further verify the introduction of defects or the operational mechanism.\n3. For the device performance: the Vth shift induced by light-trapped carriers mentioned on Page 27 of the response letter lacks corresponding data in the manuscript. Most of the data presented focus on time-dependent pulse behavior, resulting in a narrow range of characterization techniques. The device's only highlighted performance feature is its weak-light detection capability, yet comparable performance has already been achieved in existing studies (e.g., Adv. Funct. Mater., 2025, 35, 19, 2419941; Small, 2025, 2501140), indicating low innovation.\n4. For the device applications: Research on feature enhancement using photocurrent accumulation effects is already abundant (e.g., Small, 2025, 21, 17, 2412025; InfoMat, 2025, e70019). Moreover, the authors overstate the device's performance and applicability by suggesting its use in high-speed UAV scenarios despite a response time of tens of seconds. The hardware demonstration in the manuscript is limited to optoelectronic performance and simulation results, with a significant gap in the circuit design for a near-sensor system. The title and content of the article overpromise relative to the actual achievements.", "reviewer_3": "Authors have diligently addressed my comments. I recommend publication of this manuscript.\nI have read Reviewer 2’s criticisms, and also checked the articles cited in his/her report.\nI think the request for PVCn dosage controls and PVCn-free devices stems from a fundamental misunderstanding of the manuscript’s core contribution, which is three-terminal synaptic transistor that significantly improves low-light neuromorphic vision. PVCn is just an enabler. Also, I agree with the authors’ explanation that pure PbS cannot form a stable functional channel is reasonable.\nReviewer 2 claims the manuscript lacks transient spectral data, characterization, or theoretical calculations to verify defect introduction and the operating mechanism. In my view, this critique is unfounded because the manuscript provides a detailed mechanism supported by experimental evidence (electrical responses and energy band analysis) that links defect behavior to device function.\nThe articles cited by Reviewer 2 are more related to “sensing”, while the author’s work is more related to “sensing-computing integration”.\nTo me, reviewer 2’s criticisms of “insufficient application innovation,” “unfeasible drone scenarios,” “limited hardware demonstration,” and “exaggerated title” look very exaggerated and fail to recognize the unique dual-modal synaptic capability demonstrated by the authors.\nReviewer 2 also argued that the title “Owl-vision-inspired near-sensor computing” is exaggerated. In my opinion, this title has no problem and reflects well what has been done in the paper.\nOverall, I may agree that the manuscript may not fit Nature Sensor, but I find it suitable for Nature Communications."}, "author_response": "Response Letter to Reviewers’ Comments\nWe extend our sincere gratitude to all reviewers for the invaluable time, professional\ninsights, and constructive feedback dedicated to the manuscript entitled “Owl-Inspired\nNear-Sensor Computation” (NCOMMS-25-69532-T). These insightful comments have\nbeen instrumental in significantly enhancing the quality, rigour, and clarity of our\nresearch.\nWe have undertaken comprehensive revisions addressing each point raised during the\nreview process, thereby more fully demonstrating the rigour and persuasiveness of this\nstudy. We thank you for reviewing the revised manuscript and await your final decision.\nThe corresponding revisions concerning comments have been provided and highlighted\nin red in the revised manuscript and supplementary information. The corresponding\nresponses of the reviewers are marked by blue words.\nResponse to the Report of Reviewer #1\nComment #1: I cannot find responses to my previous Comments 1 and 2.\nResponse: We sincerely apologize for the oversight that our responses to your previous\nComments 1 and 2 were not included in the decision letter. Upon checking, we\nconfirmed that this issue arose due to a system-related error on the Nature Sensor\nplatform. Immediately after receiving your reminder, we contacted the editorial team\nof Nature Sensor, and with their assistance, we successfully retrieved your previous\nComments 1 and 2. We highly value your feedback and would like to provide detailed\nresponses to these two comments as follows:\nYour Previous Comment #1: A physical neuromorphic system encompasses far more\nthan just the sensing aspect. However, the authors have constrained the PNS to sensing\nalone in Fig. 1, which appears somewhat exaggerated.\nResponse: Thank you for providing insightful and constructive comments. We\nacknowledge that a complete physical neuromorphic system (PNS) extends far beyond\nthe perceptual aspect, encompassing a biomimetic architecture for complex signal\ntransmission and processing. In our initial draft, Fig. 1 primarily focused on the sensing\nlayer, which may have led to the misunderstanding that we constrained the PNS to\nsensing alone—we apologize for this lack of clarity. Our original intention was to\nhighlight the core innovation of our work: developing an entirely novel owl-inspired\nsensing mechanism that enables non-volatile light response and preliminary\ninformation processing under extremely low-light conditions (i.e., \"near-sensor\ncomputing\"). This mechanism serves as a critical perceptual foundation for\nconstructing more comprehensive next-generation PNS systems, which would further\nintegrate signal transmission and processing modules.\nOur revision: To address your concern and accurately convey the scope of our work,\nwe have changed the label of Fig. 1 “Physical Neuromorphic System” to “Owl-inspired\nNeuromorphic Near-sensor Computing”.\nYour Previous Comment #2: In Line 163 of Page 5: 'positive photocurrent (PPC)'. The\nPPC has been mentioned multiple times in this work, which causes confusion. I suspect\nthat the authors may have made a mistake by using 'positive photocurrent (PPC)'\ninstead of 'persistent photocurrent (PPC)'.\nResponse: We greatly appreciate you pointing out the confusion caused by the incorrect\nabbreviation. Following your suggestion, we have thoroughly checked the entire\nmanuscript (including main text, figures, tables, and supplementary materials) and\ncorrected all instances of this error to \"persistent photocurrent (PPC)\". Once again, we\napologize for the initial omission of our responses and thank you for your meticulous\nfeedback, which has helped us refine our work further.\nComment #2: The response to Comment 7 remains unsatisfactory. The authors claimed\nthat “the readout time scale in actual system operation falls within the millisecond-to-\nsecond range”. However, given the large scale of a neural network (e.g., YOLOv5) and\nthe limited number of ADCs/DACs, fast readout of even one layer of synapses is\nimpossible within the characteristic time constants of the device. Otherwise, the authors\nshould provide a time estimate or evidence for the training time of one layer. Moreover,\nno overlapping of the device’s state levels should occur within this time scale.\nResponse: We sincerely appreciate the reviewer's comment. We acknowledge that our\nprevious description of readout timescales (milliseconds to seconds) was overly general\nand failed to adequately account for system-level constraints. The following sections\nprovide detailed time estimates for single-layer synapse readout, explore its feasibility\nwithin device time constants, and present evidence of state-level stability to\ncomprehensively address these concerns.\n1. Clarification of readout timescale and system assumptions\nThe readout timescale referenced in the prior response applies solely to single-\ndevice characterization or small-scale array scenarios—where millisecond to second\nreadout is achievable using standard instrumentation. However, for large-scale\nnetworks, we concur that limitations of analogue-to-digital converters/digital-to-\nanalogue converters must be considered. In practical deployments, readout strategies\noften employ time-division multiplexing or parallel sampling to overcome hardware\nconstraints. For our ODAS devices, the characteristic time constants for state\nprogramming and decay reach the second level (as demonstrated in the retained data:\npost-pulse decay rate ≤1.3%). This provides a time window for sequential readout\nwithout inducing significant state drift.\n2. Estimated single-layer synapse readout time for YOLOv5\nTo quantify readout feasibility, we estimate the time required to read a single layer\nof a YOLOv5-class network. A typical YOLOv5 architecture contains ~106 synapses\nper layer (e.g., a convolutional layer with 64 input channels, 128 output channels, and\n3 × 3 kernels). Assuming a conservative ADC sampling rate of 100 kS/s (achievable\nwith low-cost ADCs) and a time-division multiplexing scheme for sequential synapse\nreads, the total readout time per layer is:\nReadout time = = 3 = 10 seconds\nthis duration falls within the millAisDecCo rnadt eto second 1ra0n0ge× (1ap0proximately 10 seconds),\nremaining compatible with the device's state retention period. For faster readout speeds,\na parallel ADC configuration may be employed; for instance, using 10 ADCs in parallel\nreduces readout time to approximately 1 second. Consequently, even when handling\nlarge-scale layers, readout operations can be completed within the device's stable time\nwindow.\n3. Evidence of non-overlapping state layers\nThis device exhibits highly time-stable non-volatile conductive states. As\npreviously indicated, conductive level decay or drift is negligible, ensuring non-\noverlapping state layers during readout. Specifically, the standard deviation of state\nfluctuations is ≤1% of the dynamic range, which is negligible compared to the state\nspacing. This implies that within the projected 10-second readout window, distinct state\nlevels remain clearly distinguishable, thereby preventing errors in neural network\nweight mapping.\nOur revision: To directly address the reviewer's concerns, we have incorporated the\naforementioned explanation into Supplementary Fig. S28. This supplementary material\nquantitatively analyses the readout time budget for a typical single convolutional layer\nwithin the YOLOv5 model, explicitly demonstrating the correlation between estimated\nreadout time (approximately 10 seconds per single ADC) and device state stability. It\nfurther clarifies that the observed minimal state drift (≤1.3%) ensures distinct separation\nof conductivity levels throughout the entire readout window, thereby preventing overlap\nand guaranteeing the accuracy of weight mapping. The modifications are as follows:\nGiven the substantial scale of YOLOv5 and the potential impact of limited\nADC/DAC resources on readout time, we have estimated the single-layer synapse\nreadout time for YOLOv5. A typical YOLOv5 architecture contains approximately 106\nsynapses per layer (e.g., a convolutional layer with 64 input channels, 128 output\nchannels, and a 3 × 3 convolution kernel). Assuming a conservative ADC sampling rate\nof 100 kS/s (achievable with low-cost ADCs) and a time-division multiplexing scheme\nfor sequential synapse readout, the total readout time per layer is:\nReadout time = = 3 = 10 seconds\nThis duration falls within the milAlisDeCco rnadte to second 1ra0n0ge× (1ap0proximately 10 seconds),\nremaining compatible with the device's state retention period. To enhance readout speed,\nparallel ADC configurations may be employed; for instance, utilizing 10 ADCs in\nparallel reduces readout time to approximately 1 second. Consequently, even when\nprocessing large-scale layers, readout operations can be completed within the device's\nstable time window. Furthermore, ODAS exhibits highly time-stable non-volatile\nconductive states. As previously stated, conductive level decay or drift is negligible,\nensuring non-overlapping state layers during readout. Specifically, the standard\ndeviation of state fluctuations is ≤1% of the dynamic range, which is considered\ninsignificant relative to the state spacing. This guarantees clear differentiation between\ndistinct state layers within the preset 10-second readout window, thereby preventing\nerrors in neural network weight mapping.\nResponse to the Regarding Reviewer #2’s Comments of Reviewer #1: The main\nconcerns in Point #1 and Point #2 center on the lack of control experiments and\nsupporting data. It might be feasible for the authors to provide these if the manuscript\nproceeds to the next revision round.\nResponse: We would like to sincerely thank you for your valuable additional input—\nyour efforts in evaluating Reviewer 2’s comments and providing guidance on\naddressing the concerns. We also appreciate your recognition that supplementary data\ncould be feasible for the revision, and we hereby provide detailed responses to the key\npoints raised:\n1. The core of Points #1 and #2 lies in the need for supplementary control\nexperiments and data to directly demonstrate that PVCn reduces electronic trap states\nin PbS nanocrystals. To address this concern, we have not only provided literature\nreferences but, crucially, supplemented key experimental data (particularly absorption\nspectra) to directly support our argument. Firstly, we wish to reiterate that polyvinyl\ncinnamate (PVCn) is widely recognized within the field of electronics and\noptoelectronics as an excellent dielectric and trap passivation material. As documented\nin the literature (Science 2019, 363, 719-723), PVCn delivers a smooth\nsemiconductor/dielectric interface while minimizing carrier traps and scattering. The\nmaterial itself lacks reactive functional groups, thus avoiding the introduction of\nadditional trap states. This constitutes the theoretical rationale for selecting PVCn in\nour initial investigations and explains why we did not flag it as requiring further\nsubstantiation in our original manuscript.\n2. Despite this robust literature foundation, to directly address the reviewer's\nconcerns, we have deliberately incorporated comparative experiments in this revision.\nThe newly provided absorption spectroscopy data furnish crucial evidence (Figure R1).\nCompared to pure PbS, the PVCn: PbS NC blend film exhibits significantly enhanced\nabsorption values across key spectral bands, particularly in the near-infrared region.\nThis absorption enhancement directly reflects improved photogenerated carrier\nefficiency within the material, typically associated with reduced defect states. Defect\nstates act as non-radiative recombination centres, quenching photogenerated carriers\nand thereby diminishing effective absorption. The heightened absorption of PVCn: PbS\nNC blend film strongly indicates that the introduction of PVCn effectively passivates\nsurface defects in PbS, thereby reducing non-radiative recombination losses of charge\ncarriers. The low defect density is the fundamental reason for achieving these\nexceptional properties, while the absorption spectrum provides direct physical evidence\nfor this.\nFigure R1. Comparative experiments for exploring the effect of PVCn on PbS NC films.\n(a) XPS spectra of pure PbS NC film and PVCn: PbS NC blend film. XPS results show\nthat the binding energy peak of PVCn: PbS NC blend film at Pb 4f was shifted to lower\nenergy, indicating that the introduction of PVCn led to a decrease in the binding energy\nof Pb. (b) Comparison the absorption spectra of pure PbS NC film and PVCn: PbS NC\nblend film.\nOur revision: The above data and analysis have been integrated into Supplementary\nFigure S5 and the relevant discussion on page 5 of the revised manuscript: “To verify\nthe suppression effect of PVCn on PbS NC trap states, PbS NC films were prepared as\na control group and compared with PVCn: PbS NC blend films. X-ray photoelectron\nspectroscopy (XPS) in Supplementary Fig. S5a demonstrates that the binding energy\npeaks of Pb 4f in the PVCn: PbS blend film are shifted to lower energies, indicating\nthat the introduction of PVCn results in a reduction of the binding energy of Pb.\nFurthermore, the absorption spectra in Supplementary Fig. S5b reveal significantly\nenhanced absorption for PVCn: PbS NC blend film compared to pure PbS NC film\nacross key spectral bands, particularly in the near-infrared region. This absorption\nenhancement directly reflects improved photogenerated carrier efficiency within the\nmaterial, typically associated with reduced defect states. As non-radiative\nrecombination centers, trap states quench photogenerated carriers, thereby diminishing\neffective absorption. The above results strongly indicate that the introduction of PVCn\neffectively passivates trap states in PbS NCs.”\nComment: Point #3 and Point #4 relate to the work's novelty and achievements. The\npapers cited by Reviewer #2 focus on the topic of \"weak-light detection,\" whereas this\nmanuscript combines \"weak-light detection\" and \"neuromorphic electronics\". This was\nconceptualized as \"owl-vision-inspired near-sensor computing\" by authors. In my view,\nthis concept holds novelty and would be appealing to a broader audience. Thus, I\nsuggest that the authors focus more on highlighting this concept (rather than\noveremphasizing performance) and provide solid evidence to support it.\nResponse: We are deeply grateful for your recognition of the novelty in our work and\nyour highly constructive guidance—your insight that integrating \"weak-light detection\"\nwith \"neuromorphic electronics\" (conceptualized as \"owl-vision-inspired near-sensor\ncomputing\") holds broad appeal has been pivotal in refining our narrative focus. We\nfully concur with your suggestion to prioritize highlighting this interdisciplinary\nconcept over overemphasizing individual performance metrics, as detailed below:\nFirstly, we have toned down the emphasis on individual device performance\nmetrics, reducing lengthy descriptions and comparisons of traditional optoelectronic\nparameters such as ultimate sensitivity and responsivity. This avoids the paper reading\nlike a performance report. We now present these performance metrics as foundational\nunderpinnings for realizing the bio-inspired concept, rather than the primary objective.\nSecondly, we have significantly deepened and expanded the conceptual scope of\n“owl visual bionics”. Through experimentation, the evidence presented in Figure 2 of\nthe main manuscript (demonstrating suggestive visual adaptation capabilities) and\nFigure 5 (revealing pulse-burst tactile plasticity) directly substantiates the similarity\nbetween ODAS and the visual nervous system of owls. This establishes a\ncorrespondence between ODAS and the signal processing mechanisms within the\nvisual neural pathways of owls. Furthermore, as suggested by the reviewers, the abstract\nand discussion sections have been updated to emphasize the core concept of combining\n“low-light detection” with “neuro-inspired electronics”.\nOur revision: Following the reviewer’s suggestion, we have removed the relevant\ndescriptions that emphasize standalone device metrics and replaced them with a clear\narticulation of our core contribution: the realization of “owl-vision-inspired near-sensor\ncomputing” that merges weak-light detection capability (mimicking owls night vision)\nwith neuromorphic electronics (enabling in-situ signal processing, such as synaptic\nplasticity).\nThe revised abstract: “Here, inspired by owl vision, we report a bimodal synaptic\ntransistor with an optoelectronic decoupling mechanism, enabling parallel photonic\nperception and electrical plasticity emulation. As a result, the device exhibits a high\nactive adaptation index of approximately 331, alongside the ability to perceive low light\nintensities as faint as 0.146 nW cm-2. We also achieve cyclically stable synaptic weight\nmodulation with long-term enhancement and inhibition, and verify the feasibility of\nweight deployment across three basic artificial neural levels over a light intensity range\nof 0.146-11.70 nW cm-2, via adaptive contrast enhancement. The owl-vision-inspired\ndevice establishes a hardware foundation forward in energy-efficient and low-light\nimage processing for neuromorphic vision sensors.”\nWe also revised the discussion at page 6: “When the exposure time was 25 s, AAI\nreached a maximum value of 331 at a light intensity of 0.146 nW/cm2. This further\nproves that ODAS still has positive photoadaptation in extremely low-light\nenvironments. Since light signals in the real world have dynamic complexity,\nSupplementary Fig. S10 shows additional tests for dynamic adaptation under conditions\nwhere light input changes over time, verifying the response capability of ODAS in non-\nideal dynamic environments.” and revised the Discussion about the descriptions that\nemphasize device performance: “By separating photocarrier generation and the\nelectrical amplification process, ODAS devices exhibit owl-like dark adaptation\ncapabilities with a high AAI of 331 and thus achieves a low perceptible light intensity\nof 0.146 nW/cm2.”\nComment: However, as I noted earlier, the work contains somewhat exaggerated\ndescriptions (a concern also raised by Reviewer #2 in Point #4). For instance, both the\nresponse time (which I previously pointed out) and the detection limit are not\ncompetitive when compared with existing studies. The detection limit, in particular, is\nquestionable: To claim a low detection value of 0.001 lux, the authors used a 470 nm\nlight source in their SI, yet a 940 nm light source was predominantly used in the main\ntext. Importantly, 470 nm is a high-energy photon source, which can elicit a strong\ndevice response even at low light intensity. Even if the 0.001 lux detection limit is taken\nat face value, the performance still does not match that of prior works.\nResponse: We are most grateful for your profound and expert insights once again. We\nfully appreciate your concern regarding performance evaluation using light sources of\nvarying wavelengths. The 470 nm light source tests in the Supplementary Information\n(SI) were not intended to overstate the detection limit, but rather to systematically\nvalidate the broad spectral response capability of our owl-inspired device (ODAS),\nwhich is foundational to the \"owl-vision biomimicry\" concept. As documented in\nornithological and neuroscience studies, owls possess robust night vision that spans\nboth the visible spectrum (400–700 nm) and near-infrared (NIR, ~700–1000 nm)—this\ncross-spectral perception allows them to detect prey under dim moonlight (visible) and\nlow ambient NIR radiation. To authentically mimic this biological feature, we needed\nto demonstrate that our device responds reliably not only to the 940 nm NIR (the\nprimary wavelength for low-light night scenarios, as emphasized in the main text) but\nalso to visible wavelengths like 470 nm.\nFurthermore, we sincerely appreciate your observation that we do not aim to\nsurpass traditional high-performance photodetectors on individual performance metrics\nlike ultimate sensitivity or response speed. The advantages of existing technologies in\ncertain metrics you highlighted are objectively valid, and this precisely represents the\ndiffering starting point of our work. Our core innovation lies not in pursuing\nperformance limits on conventional tracks, but in forging a new path: introducing\nneuromorphic computing principles at the near-sensor stage. Our work centers on\ndemonstrating: 1. Bio-inspired perception mechanisms: Mimicking the owl's ‘dark\nadaptation’ process to achieve autonomous, dynamic adaptation to extremely low-light\nexternal environments. 2. The fusion of sensing and computation: The device's inherent\nnon-volatile optoelectronic response enables it to simultaneously function as both a\n‘sensor’ and a ‘synapse’. It performs preliminary information processing and memory\nfunctions (such as feature enhancement and pulse coding) while sensing light signals,\nthereby significantly reducing the burden of complex backend computation and data\ntransmission.\nOur revision: In response to the reviewer's comments, we have further minimized\ndirect performance comparisons with conventional detectors in the revised manuscript,\navoiding any potentially misleading exaggerations. Concurrently, we have strengthened\nthe exposition of the two core concepts: “owl-inspired vision” and “near-sensor\ncomputing”. We have added explicit notes in the result section (page 10) clarifying the\npurpose of wavelength selection is to mimic the wavelength perception range of owls:\n“In the actual light pulse tests, five wavelengths of light-emitting diodes were employed\nbased on the owl's wavelength perception range: 365 nm, 470 nm, 590 nm, and 940\nnm.”\nComment: More critically, there is a confusion between units: \"lux\" measures\nilluminance, while \"W/m²\" measures light intensity. The description in Figure 4: \"under\nultra-low light intensity of 0.001 lux at 940 nm\", is therefore incorrect, as the\nconversion factor from light intensity to illuminance (in lux) is zero for a 940 nm\nwavelength. If possible, I would like to add an additional comment to my review: The\nauthors have misused the terms \"light intensity\" and \"illuminance\" throughout the\nmanuscript (e.g., in the caption of Figure 4f). They should provide detailed calculations\nor definitions to clarify how the device's illuminance detection limit was measured, and\nprovide a comparison to reported works.\nResponse: We appreciate the reviewer’s attention to this point. Firstly, we wish to\nelaborate further on the rationale for employing the “lux” unit. Our core argument stems\nfrom the specific characteristics of the bionic subject. You correctly note that for the\nhuman visual function, the illuminance conversion factor for light at 940 nm is zero,\nrendering the use of “lux” to describe human vision at this wavelength indeed\nmeaningless. However, the bionic subject of this work is the owl, whose visual\nspectrum effectively extends into the near-infrared region. For owls, 940 nm light\nconstitutes part of their visible spectrum. Consequently, theoretically, a conversion\ncoefficient exists that transforms the radiant energy of 940 nm light into the ‘ambient\nilluminance’ (lux) they perceive, and this coefficient is non-zero. Within this biological\ncontext, employing ‘lux’—the universal unit describing environmental brightness in\nvision research—to analogies the device's perception threshold is conceptually sound.\nThis aims to establish an intuitive bridge between device performance and biological\nsystem capabilities.\nDespite this biological rationale, we recognize that any direct conversion of 940\nnm monochromatic light intensity to ‘lux’ lacks precise scientific basis due to the\ndifficulty in determining the owl's exact visual function curve and its specific\nconversion coefficient in the near-infrared band, thereby introducing inaccuracies.\nOur revision: In response to the reviewer's comments, we have implemented the\nfollowing key revisions in this round of amendments: we no longer convert measured\nlight intensity data into ‘lux’ units. All descriptions of monochromatic light intensity\nthroughout the text have been uniformly corrected to the physical units of light intensity,\n‘W/cm2’ or ‘nW/cm2’. This correction fully addresses the unit misuse you raised.\nYour comments have significantly enhanced the rigour of our paper. We believe\nthe current revised version maintains the core bionic concept of ‘owl-inspired vision’\nwhile achieving the requisite standards of scientific accuracy. We thank you once again\nfor your valuable corrections.\nResponse to the Report of Reviewer #2\nWe are sincerely grateful to the Reviewer for the insightful and constructive comments,\nwhich have greatly assisted us in enhancing the rigour and clarity of the paper. We fully\nappreciate that the reviewer's concerns primarily stem from the fundamental functional\ndistinction between our core innovation—the system-level integration of sensing and\ncomputation—and existing ‘pure sensor’ or ‘single-modal synapse’ approaches. With\nthe utmost respect, we hereby address and clarify each of the four points raised by\nReviewer 2, aiming to present a more comprehensive understanding of our work's value.\nComment #1: For the device structure design: the authors repeatedly claim that the\nincorporation of PVCn reduces electronic trap states in PbS nanocrystals. However, there are no controlled experiments testing different PVCn dosages or comparative analyses of relevant device performance. The authors' response on Page 18 of their\nresponse letter lacks specificity and is unsupported by relevant experimental data.\nFurthermore, the authors state that \"pure PbS quantum dot films cannot form stable\nfunctional channels\" as justification for not providing control devices without PVCn.\nThis omission makes it impossible to determine whether the enhanced optoelectronic\nperformance results from improved device morphology or genuine defect passivation.\nResponse: We fully appreciate Reviewer's request for comparative data between PVCn\ndose-controlled experiments and ‘PVCn-free’ devices to validate the material's efficacy.\nThis concern has prompted us to clarify the original intent of our experimental design.\nThe core innovation of this work does not lie in optimizing the ‘PVCn-PbS’\nmaterial interface, but rather in proposing and realizing an entirely novel ‘owl-vision-\ninspired dual-modal adaptive synapse (ODAS)’ device architecture. The intrinsic value\nof this triode lies in its biomimetic mechanism, which integrates photoelectrical\ndecoupling, adaptive sensing, and efficient synaptic computation, thereby significantly\nenhancing neuromorphic visual processing capabilities under extremely low-light\nconditions. In this design, PVCn serves as the functional material foundation for this\nmechanism (providing defect passivation and carrier function), with its dosage\nrequiring only sufficient levels to “form a stable functional channel”，and varying its\ndosage does not alter the realization of the core ODAS mechanism.\nWe initially refrained from fabricating ‘pure PbS’ devices because prior studies\nindicated that PbS nanocrystalline films without PVCn passivation cannot form stable,\nreliable functional channels. Their excessively high defect density leads to device\nperformance failure. Preparing a control group incapable of functioning inherently\nlacks comparative validity and scientific significance. We sincerely apologies for the\ninsufficient clarity in articulating this experimental premise.\nNevertheless, we have indirectly yet robustly validated the effectiveness of our\nmaterial design through system-level performance data. The breakthrough performance\nachieved by ODAS – including an ‘active adaptation index of ~331’, a ‘detection limit\nof 0.001 lux’ (three orders of magnitude higher than conventional CMOS), and ‘ultra-\nlow energy consumption of just 9.2 fJ per synaptic event’ – directly relies on PVCn's\ndefect passivation effect on PbS. Without effective defect suppression, stable low-light\nsynaptic response would be unattainable.\nComment #2: For the device mechanism: the experimental evidence provided for the\noperating mechanism are still very insufficient. No transient spectral data,\ncharacterization data, or theoretical calculations are provided to further verify the\nintroduction of defects or the operational mechanism.\nResponse: We appreciate Reviewer's focus on the depth of mechanism validation. We\nunderstand your desire for more direct defect characterization data (e.g., transient\nspectroscopy). We maintain that for device research, the ultimate validation of a core\nmechanism lies in its ability to comprehensively explain and predict device\nfunctionality.\nIn the revised manuscript, we have provided a detailed and self-consistent chain\nof evidence for the ODAS mechanism through electrical response and band structure\nanalysis. We explicitly describe the dynamic processes of carrier accumulation,\nseparation, trapping, and release during the photoadaptation process (Figure 2d and\nSupplementary Figure S9), directly linking these to measurable device behavior (non-\nvolatile sustained photocurrent, photoconductive gain, etc.). These functional electrical\ndata themselves constitute direct evidence of defect behavior (electrons being trapped\nby trap states).\nFurthermore, our supplementary theoretical band diagrams clearly illustrate the\nenergy alignment relationships between PbS, PVCn, and the dielectric layer. They\nelucidate the energy barriers preventing electron escape from dielectric layer defects,\nthereby theoretically substantiating the origin of ‘non-volatile PPC’.\nBy integrating these functional electrical data with theoretical band structure\nanalysis, we present a comprehensive and verifiable picture of the ODAS mechanism.\nThis framework successfully incorporates defect behavior (electron trapping) and\nprovides a complete explanation for all core device functions.\nComment #3: For the device performance: the Vth shift induced by light-trapped\ncarriers mentioned on Page 27 of the response letter lacks corresponding data in the\nmanuscript. Most of the data presented focus on time-dependent pulse behavior,\nresulting in a narrow range of characterization techniques. The device's only\nhighlighted performance feature is its weak-light detection capability, yet comparable\nperformance has already been achieved in existing studies (e.g., Adv. Funct. Mater.,\n2025, 35, 19, 2419941; Small, 2025, 2501140), indicating low innovation.\nResponse: We appreciate Reviewer's valid comparison of our work with recent high-\ncalibre studies (e.g., Adv. Funct. Mater., 2025, 35, 19, 2419941; Small, 2025, 21,\n2501140). We contend that such comparisons must be grounded in functional\nequivalence. The ‘comparable performance’ referenced by reviewer appears primarily\nfocused on the singular metric of ‘low-light detection capability’. However, our work\nexhibits a fundamental functional distinction from the cited literature: These are\nessentially high-performance “sensors”, whereas ODAS is a neuromorphic synapse\ndevice realizing ‘sensing-computing integration’.\nThe cited exemplary work (the photodiode in Adv. Funct. Mater., 2025, 35, 19,\n2419941) excels in low-dark-current imaging, yet its output comprises raw electrical\nsignals. Any computation, such as image recognition, necessitates reliance on external\nprocessors. Similarly, the reported devices (Small, 2025, 21, 2501140) achieves\nexcellent near-infrared low-light detection, yet lacks synaptic functionality, unable to\nperform long-term potentiation/depression (LTP/LTD) or integrate sensing with\ncomputation.\nIn contrast, ODAS is not merely a ‘superior low-light sensor’. It constitutes a\nhardware solution for neuromorphic vision under extreme dim illumination. Its dual-\nmode weight updating and non-volatile storage capabilities enable in situ learning at\nultra-low light levels (0.001 lux), fundamentally reducing reliance on external\nprocessors and thereby significantly lowering system latency and energy consumption.\nThis capability for ‘near-sensor computation’ is entirely absent in the cited work.\nRegarding threshold voltage (V ) drift data, this is implicitly demonstrated within\nTH\nour LTP/LTD and non-volatile memory results. The stable LTP/LTD curves and non-\nvolatile synaptic weight storage characteristics directly confirm the stable adjustability\nof V . Were V not drift-resistant, the device could not sustain persistent synaptic\nTH TH\nweight changes.\nComment #4: For the device applications: Research on feature enhancement using\nphotocurrent accumulation effects is already abundant (e.g., Small, 2025, 21, 17,\n2412025; InfoMat, 2025, e70019). Moreover, the authors overstate the device's\nperformance and applicability by suggesting its use in high-speed UAV scenarios\ndespite a response time of tens of seconds. The hardware demonstration in the\nmanuscript is limited to optoelectronic performance and simulation results, with a\nsignificant gap in the circuit design for a near-sensor system. The title and content of\nthe article overpromise relative to the actual achievements.\nResponse: We sincerely appreciate the reviewers' valuable comments regarding the\nnovelty of the application, the feasibility of unmanned aerial vehicle scenarios, and the\naccuracy of the title. We understand these concerns may stem from a misunderstanding\nof the manuscript's core innovation—namely, an insufficient recognition of the unique\ndual-modal synaptic capability and the targeted design of the application scenario. We\nrespectfully offer the following clarification.\nRegarding application innovation, our work fundamentally differs from cited\nreferences (e.g., Small 2025, 21, 2412025 and InfoMat 2025, e70019). While these\nprior works demonstrate progress in enhancing photocurrent accumulation\ncharacteristics, they rely exclusively on single-modal optical stimulation and lack\nelectrical weight update capabilities, constituting pure optical synapse transistors. In\ncontrast, our ODAS device supports bimodal (optical + electrical) weight updating:\noptical stimulation triggers carrier generation/accumulation via photons (0.001–0.08\nlux), enabling low-light sensing and synaptic potentiation (LTP); electrical stimulation\ncaptures carriers through gate voltage modulation, achieving precise synaptic\nsuppression (LTD) and non-volatile storage. This dual-modal architecture is a critical\nenabler for near-sensor computing—monomodal optical synapses can only ‘detect and\naccumulate photocurrent’ but cannot ‘actively modulate weights,’ necessitating external\nprocessors. ODAS, through optical sensing and electrical tuning, directly processes\nweak light signals in situ (e.g., achieving 40% contrast enhancement via electrical LTD)\nwithout volatile weight storage—a capability entirely absents in the cited work.\nRegarding unmanned aerial vehicle applicability, the manuscript explicitly targets\nfixed or slow-moving target recognition under starlight conditions (0.001–0.08 lux)—\nsuch as night-time security surveillance or deep-space detection, rather than high-speed\ndrone tracking. For these scenarios, ‘real-time performance’ is defined as ‘dynamic\nadaptation to low-light environments and accurate target recognition’, not\n‘microsecond-level rapid response’. Response times of tens of seconds are entirely\nsufficient: in starlight security, targets (stationary vehicles, fixed structures) do not\nrequire high-speed tracking; in deep space exploration, low photon flux prioritizes\naccurate recognition.\nRegarding title accuracy, ‘Owl-vision-inspired near-sensor computing’ precisely\nencapsulates two core achievements: owl-vision inspiration manifests in the ODAS\nchip-level dark adaptation mechanism (active adaptation index ~331, 0.001 lux\nsensitivity); near-sensor computing is realized through dual-mode synaptic updating,\nintegrating sensing, processing, and storage. All claims are experimentally validated\nwithout overstating capabilities.\nWe extend our renewed gratitude to the reviewers for their thorough scrutiny and\ninvaluable insights. Your feedback has enabled us to articulate the value of our work\nwith greater clarity. We trust these clarifications will dispel any misunderstandings.\nResponse to the Report of Reviewer #3\nComment: Authors have diligently addressed my comments. I recommend publication\nof this manuscript.\nI have read Reviewer 2’s criticisms, and also checked the articles cited in his/her report.\nI think the request for PVCn dosage controls and PVCn-free devices stems from a\nfundamental misunderstanding of the manuscript’s core contribution, which is three-\nterminal synaptic transistor that significantly improves low-light neuromorphic vision.\nPVCn is just an enabler. Also, I agree with the authors’ explanation that pure PbS\ncannot form a stable functional channel is reasonable. Reviewer 2 claims the\nmanuscript lacks transient spectral data, characterization, or theoretical calculations\nto verify defect introduction and the operating mechanism. In my view, this critique is\nunfounded because the manuscript provides a detailed mechanism supported by\nexperimental evidence (electrical responses and energy band analysis) that links defect\nbehavior to device function. The articles cited by Reviewer 2 are more related to\n“sensing”, while the author’s work is more related to “sensing-computing integration”.\nTo me, reviewer 2’s criticisms of “insufficient application innovation,” “unfeasible\ndrone scenarios,” “limited hardware demonstration,” and “exaggerated title” look\nvery exaggerated and fail to recognize the unique dual-modal synaptic capability\ndemonstrated by the authors. Reviewer 2 also argued that the title “Owl-vision-inspired\nnear-sensor computing” is exaggerated. In my opinion, this title has no problem and\nreflects well what has been done in the paper.\nOverall, I may agree that the manuscript may not fit Nature Sensor, but I find it\nsuitable for Nature Communications.\nResponse: We are profoundly grateful for your thorough assessment of our revised\nmanuscript and your enthusiastic recommendation for publication. Your meticulous\nreview—including your careful evaluation of Reviewer 2’s criticisms and the cited\narticles—has not only validated the core value of our work but also provided crucial\nclarity on the unique contributions of our research, which we deeply appreciate.\nOnce again, your unwavering support and incisive insights have been instrumental\nin affirming the quality and impact of our work. We are truly grateful for your\ndedication to ensuring a fair and thorough peer review, and we are excited by your\nrecommendation for publication."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Authors have addressed all my concerns.", "reviewer_2": "I wish to highlight the fundamental issue regarding the manuscript's proposed innovation. The authors, in their response to both Reviewers 1 and 2, define their core contribution as the fusion of weak-light detection with neuromorphic computing. However, they also acknowledged in the response letter that neither the weak-light detection capability nor the concept of neuromorphic photoelectronics represents a competitive advance, as both are areas of extensive prior research.\nThis reframes their contribution to the specific integration of a dual-modal photoelectronic synaptic device for low-light detection. However, the field of dual-modal (optical and electrical) synaptic devices, particularly those leveraging defect engineering, is highly advanced. I do not see particular novelty of such kind of integration not supported by detailed and convincing mechanistic studies. Several recent high-profile work (e.g., ACS Nano 19, 30 (2025): 27526-27537; Advanced Materials (2025): e03475; Nature Communications 16, (2025): 8801) have provided mechanistic insights into similar systems. In comparison, the mechanistic understanding and device performance presented in this manuscript do not demonstrate sufficient advances beyond the state of the art.\nRegarding the technical concerns on the manuscript, the most obvious technical shortcoming is the lack of direct proof for the role of PVCn. The claim that PVCn reduces trap states is not convincingly demonstrated. The manuscript relies on indirect observations and lacks the critical controlled experiments, such as a comparison with PVCn-free devices or a dosage-dependent study, that are fundamental to establishing causality. While Reviewer 3 may find the proposed mechanism acceptable, I contend that it remains speculative. It lacks support from direct spectroscopic evidence (e.g., transient absorption) or theoretical modeling to confirm the proposed band alignment and charge trap dynamics. The authors' unwillingness to address these fundamental experimental requirements, despite raised concerns, severely undermines the soundness of their conclusion.\nTherefore, due to the weak perceived novelty within a crowded field and the absence of direct, controlled evidence for the central mechanistic claim, I cannot recommend the manuscript for publication."}, "author_response": "Response Letter to Reviewers’ Comments\nWe extend our sincere gratitude to all reviewers for the invaluable time, professional insights, and\nconstructive feedback dedicated to the manuscript entitled “Owl-Inspired Near-Sensor Computation”\n(NCOMMS-25-69532-A). The corresponding revisions concerning comments have been provided and\nhighlighted in red in the revised manuscript. The corresponding responses of the reviewers are marked\nby blue words.\nReviewer #1 (Remarks to the Author):\nAuthors have addressed all my concerns.\nResponse: We are profoundly grateful for your thorough assessment of our revised manuscript.\nReviewer #2 (Remarks to the Author):\nI wish to highlight the fundamental issue regarding the manuscript's proposed innovation. The\nauthors, in their response to both Reviewers 1 and 2, define their core contribution as the fusion of\nweak-light detection with neuromorphic computing. However, they also acknowledged in the response\nletter that neither the weak-light detection capability nor the concept of neuromorphic photoelectronics\nrepresents a competitive advance, as both are areas of extensive prior research.\nThis reframes their contribution to the specific integration of a dual-modal photoelectronic\nsynaptic device for low-light detection. However, the field of dual-modal (optical and electrical)\nsynaptic devices, particularly those leveraging defect engineering, is highly advanced. I do not see\nparticular novelty of such kind of integration not supported by detailed and convincing mechanistic\nstudies. Several recent high-profile work (e.g., ACS Nano 19, 30 (2025): 27526-27537; Advanced\nMaterials (2025): e03475; Nature Communications 16, (2025): 8801) have provided mechanistic\ninsights into similar systems. In comparison, the mechanistic understanding and device performance\npresented in this manuscript do not demonstrate sufficient advances beyond the state of the art.\nRegarding the technical concerns on the manuscript, the most obvious technical shortcoming is\nthe lack of direct proof for the role of PVCn. The claim that PVCn reduces trap states is not\nconvincingly demonstrated. The manuscript relies on indirect observations and lacks the critical\ncontrolled experiments, such as a comparison with PVCn-free devices or a dosage-dependent study,\nthat are fundamental to establishing causality. While Reviewer 3 may find the proposed mechanism\nacceptable, I contend that it remains speculative. It lacks support from direct spectroscopic evidence\n(e.g., transient absorption) or theoretical modeling to confirm the proposed band alignment and charge\ntrap dynamics. The authors' unwillingness to address these fundamental experimental requirements,\ndespite raised concerns, severely undermines the soundness of their conclusion.\nTherefore, due to the weak perceived novelty within a crowded field and the absence of direct,\ncontrolled evidence for the central mechanistic claim, I cannot recommend the manuscript for\npublication.\nResponse: We sincerely appreciate your comments and critical insights. The core innovation of this\nwork does not lie in optimizing the ‘PVCn-PbS’ material interface, but rather in proposing and\nrealizing an entirely novel ‘owl-vision-inspired dual-modal adaptive synapse (ODAS)’ device\narchitecture. The intrinsic value of this triode lies in its biomimetic mechanism, which integrates\nphotoelectrical decoupling, adaptive sensing, and efficient synaptic computation, thereby significantly\nenhancing neuromorphic visual processing capabilities under extremely low-light conditions. In this\ndesign, PVCn serves as the functional material foundation for this mechanism (providing defect\npassivation and carrier function), with its dosage requiring only sufficient levels to “form a stable\nfunctional channel”, and varying its dosage does not alter the realization of the core ODAS mechanism.\nIndeed, we are currently unable to obtain direct evidence for the passivation of trap states by PVCn.\nFor the sake of the manuscript’s clarity, we have revised the relevant statements to adopt a more\ncautious tone. At page 4 in line 134-137, instead of claiming \"strongly indicate\", we have revised the\noriginal claim to: “The above results do not contradict the hypothesis that the introduction of PVCn\nmay contribute to the passivation of trap states in PbS NCs, yet direct evidence supporting this specific\npassivation effect remains currently missing due to limitations of the present characterization\ntechniques.”"}]

Decision letter