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Nov 22, 2024; e2409258.

Hardware-Feasible and Efficient N-Type Organic Neuromorphic Signal Recognition via Reservoir Computing.

Riping Liu, Yifei He, Xiuyuan Zhu, Jiayao Duan, Chuan Liu, Zhuang Xie, Iain McCulloch, Wan Yue
Author Information
  1. Riping Liu: Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, 510275, P. R. China.
  2. Yifei He: Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, 510275, P. R. China.
  3. Xiuyuan Zhu: Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, 510275, P. R. China.
  4. Jiayao Duan: Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, 510275, P. R. China.
  5. Chuan Liu: State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, P. R. China.
  6. Zhuang Xie: Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, 510275, P. R. China.
  7. Iain McCulloch: Andlinger Center for Energy and the Environment, Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, 08544, USA.
  8. Wan Yue: Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, 510275, P. R. China. ORCID
PMID: 39578330 DOI: 10.1002/adma.202409258

Abstract

Organic electrochemical synaptic transistors (OESTs), inspired by the biological nervous system, have garnered increasing attention due to their multifunctional applications in neuromorphic computing. However, the practical implementation of OESTs for signal recognition-particularly those utilizing n-type organic mixed ionic-electronic conductors (OMIECs)-still faces significant challenges at the hardware level. Here, a state-of-the-art small-molecule n-type OEST integrated within a physically simple and hardware feasible reservoir-computing (RC) framework for practical temporal signal recognition is presented. This integration is achieved by leveraging the adjustable synaptic properties of the n-OEST, which exhibits tunable nonlinear short-term memory, transitioning from volatility to nonvolatility, and demonstrating adaptive temporal specificity. Additionally, the nonvolatile OEST offers 256 conductance levels and a wide dynamic range (≈147) in long-term potentiation/depression (LTP/LTD), surpassing previously reported n-OESTs. By combining volatile n-OESTs as reservoirs with a single-layer perceptron readout composed of nonvolatile n-OEST networks, this physical RC system achieves substantial recognition accuracy for both handwritten-digit images (94.9%) and spoken digit (90.7%), along with ultrahigh weight efficiency. Furthermore, this system demonstrates outstanding accuracy (98.0%) by grouped RC in practical sleep monitoring, specifically in snoring recognition. Here, a reliable pathway for OMIEC-driven computing is presented to advance bioinspired hardware-based neuromorphic computing in the physical world.

Keywords

neural network hardware n‐type small molecules organic electrochemical synaptic transistors organic mixed ionic‐electronic conductors reservoir computing

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Grants

  1. 2022YFA1206600/National Key R&D Program
  2. 22275212/National Natural Science Foundation of China
  3. 22075325/National Natural Science Foundation of China
  4. 23yxqntd002/Fundamental Research Funds for the Central Universities, Sun Yat-sen University
  5. 24qnpy026/Fundamental Research Funds for the Central Universities, Sun Yat-sen University
  6. 2024A1515010601/Natural Science Foundation of Guangdong Province
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Created with Highcharts 10.0.0computingsynapticsystempracticalorganichardwareRCrecognitionOrganicelectrochemicaltransistorsOESTsneuromorphicsignaln-typemixedconductorsOESTtemporalpresentedn-OESTnonvolatilen-OESTsphysicalaccuracyinspiredbiologicalnervousgarneredincreasingattentionduemultifunctionalapplicationsHoweverimplementationrecognition-particularlyutilizingionic-electronicOMIECs-stillfacessignificantchallengeslevelstate-of-the-artsmall-moleculeintegratedwithinphysicallysimplefeasiblereservoir-computingframeworkintegrationachievedleveragingadjustablepropertiesexhibitstunablenonlinearshort-termmemorytransitioningvolatilitynonvolatilitydemonstratingadaptivespecificityAdditionallyoffers256conductancelevelswidedynamicrange≈147long-termpotentiation/depressionLTP/LTDsurpassingpreviouslyreportedcombiningvolatilereservoirssingle-layerperceptronreadoutcomposednetworksachievessubstantialhandwritten-digitimages949%spokendigit907%alongultrahighweightefficiencyFurthermoredemonstratesoutstanding980%groupedsleepmonitoringspecificallysnoringreliablepathwayOMIEC-drivenadvancebioinspiredhardware-basedworldHardware-FeasibleEfficientN-TypeNeuromorphicSignalRecognitionviaReservoirComputingneuralnetworkn‐typesmallmoleculesionic‐electronicreservoir

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