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

Byung Chul Jang; Sungkyu Kim; Sang Yoon Yang; Jihun Park; Jun Hwe Cha; Jungyeop Oh; Junhwan Choi; Sung Gap Im; Vinayak P. Dravid; Sung Yool Choi

doi:10.1021/acs.nanolett.8b04023

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

Byung Chul Jang, Sungkyu Kim, Sang Yoon Yang, Jihun Park, Jun Hwe Cha, Jungyeop Oh, Junhwan Choi, Sung Gap Im, Vinayak P. Dravid, Sung Yool Choi

Research output: Contribution to journal › Article › peer-review

165 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	839-849
Number of pages	11
Journal	Nano Letters
Volume	19
Issue number	2
DOIs	https://doi.org/10.1021/acs.nanolett.8b04023
State	Published - 13 Feb 2019

Keywords

Flexible memristor
artificial neural network (ANN)
electrochemical metallization (ECM)
neuromorphic system
quantized conductance

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10.1021/acs.nanolett.8b04023

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title = "Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System",

abstract = "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.",

keywords = "Flexible memristor, artificial neural network (ANN), electrochemical metallization (ECM), neuromorphic system, quantized conductance",

author = "Jang, {Byung Chul} and Sungkyu Kim and Yang, {Sang Yoon} and Jihun Park and Cha, {Jun Hwe} and Jungyeop Oh and Junhwan Choi and Im, {Sung Gap} and Dravid, {Vinayak P.} and Choi, {Sung Yool}",

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AU - Jang, Byung Chul

AU - Kim, Sungkyu

AU - Yang, Sang Yoon

AU - Park, Jihun

AU - Cha, Jun Hwe

AU - Oh, Jungyeop

AU - Choi, Junhwan

AU - Im, Sung Gap

AU - Dravid, Vinayak P.

AU - Choi, Sung Yool

PY - 2019/2/13

Y1 - 2019/2/13

N2 - 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.

AB - 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.

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

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