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^*

^*Corresponding author for this work

Materials Science and Engineering

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

167 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 (US)
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 - Feb 13 2019

Funding

This research was supported by the Global Frontier Center for Advanced Soft Electronics (CASE-2011-0031640 and CASE-2017M3A6A5052509), the Brain Korea 21 Plus Project of School of Electrical Engineering of KAIST in 2018, and the Creative Research Program of the ETRI (18ZB11400). This work also made use of the EPIC facility of Northwestern University’s NUANCE Center, which has received support from the Soft and Hybrid Nanotechnology Experimental (SHyNE) Resource (NSF ECCS-1542205); the MRSEC program (NSF DMR-1121262) at the Materials Research Center; the International Institute for Nanotechnology (IIN); the Keck Foundation; and the state of Illinois, through the IIN.

Keywords

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

ASJC Scopus subject areas

Bioengineering
General Chemistry
General Materials Science
Condensed Matter Physics
Mechanical Engineering

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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.",

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AU - Cha, Jun Hwe

AU - Oh, Jungyeop

AU - Choi, Junhwan

AU - Im, Sung Gap

AU - Dravid, Vinayak P.

AU - Choi, Sung Yool

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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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