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Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System

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Download Hi-Res ImageDownload to MS-PowerPointCite This:Nano Lett. 2019, 19, 2, 839-849
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    Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System
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    • Byung Chul Jang
      Byung Chul Jang
      School of Electrical Engineering, Graphene/2D Materials Research Center, KAIST, Daejeon 34141, Korea
      More by Byung Chul Jang
    • Sungkyu Kim
      Sungkyu Kim
      Department of Materials Science and Engineering and NUANCE Center, Northwestern University, Evanston, Illinois 60208, United States
      More by Sungkyu Kim
    • Sang Yoon Yang
      Sang Yoon Yang
      School of Electrical Engineering, Graphene/2D Materials Research Center, KAIST, Daejeon 34141, Korea
      More by Sang Yoon Yang
    • Jihun Park
      Jihun Park
      School of Electrical Engineering, Graphene/2D Materials Research Center, KAIST, Daejeon 34141, Korea
      More by Jihun Park
    • Jun-Hwe Cha
      Jun-Hwe Cha
      School of Electrical Engineering, Graphene/2D Materials Research Center, KAIST, Daejeon 34141, Korea
      More by Jun-Hwe Cha
    • Jungyeop Oh
      Jungyeop Oh
      School of Electrical Engineering, Graphene/2D Materials Research Center, KAIST, Daejeon 34141, Korea
      More by Jungyeop Oh
    • Junhwan Choi
      Junhwan Choi
      Department of Chemical and Biomolecular Engineering, Graphene/2D Materials Research Center, KAIST, Daejeon 34141, Korea
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    • Sung Gap Im
      Sung Gap Im
      Department of Chemical and Biomolecular Engineering, Graphene/2D Materials Research Center, KAIST, Daejeon 34141, Korea
      More by Sung Gap Im
      Orcidhttp://orcid.org/0000-0002-2802-6398
    • Vinayak P. Dravid
      Vinayak P. Dravid
      Department of Materials Science and Engineering and NUANCE Center, Northwestern University, Evanston, Illinois 60208, United States
      More by Vinayak P. Dravid
      Orcidhttp://orcid.org/0000-0002-6007-3063
    • Sung-Yool Choi*
      Sung-Yool Choi
      School of Electrical Engineering, Graphene/2D Materials Research Center, KAIST, Daejeon 34141, Korea
      *E-mail: [email protected]
      More by Sung-Yool Choi
      Orcidhttp://orcid.org/0000-0002-0960-7146
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    Nano Letters

    Cite this: Nano Lett. 2019, 19, 2, 839–849
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    https://doi.org/10.1021/acs.nanolett.8b04023
    Published January 4, 2019
    Copyright © 2019 American Chemical Society
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    Abstract

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    With the advent of artificial intelligence (AI), memristors have received significant interest as a synaptic building block for neuromorphic systems, where each synaptic memristor should operate in an analog fashion, exhibiting multilevel accessible conductance states. Here, we demonstrate that the transition of the operation mode in poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3)-based flexible memristor from conventional binary to synaptic analog switching can be achieved simply by reducing the size of the formed filament. With the quantized conductance states observed in the flexible pV3D3 memristor, analog potentiation and depression characteristics of the memristive synapse are obtained through the growth of atomically thin Cu filament and lateral dissolution of the filament via dominant electric field effect, respectively. The face classification capability of our memristor is evaluated via simulation using an artificial neural network consisting of pV3D3 memristor synapses. These results will encourage the development of soft neuromorphic intelligent systems.

    Copyright © 2019 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.nanolett.8b04023.

    • Electrical and mechanical characteristics of the binary pV3D3 memristor, discussion about the method of gradual increase of compliance current for the formation of thin filament, synaptic behaviors of pV3D3 memristor, discussion about voltage and current sweep modes, details about the device-to-simulation for the face classification, conductance distributions of the synapse array for the class 2 and 3 persons, and the pattern recognition results for MNIST handwritten digits (PDF)

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    This article is cited by 161 publications.

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    Cite this: Nano Lett. 2019, 19, 2, 839–849
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    https://doi.org/10.1021/acs.nanolett.8b04023
    Published January 4, 2019
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