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

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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.
Publisher
AMER CHEMICAL SOC
Issue Date
2019-01
Language
English
Article Type
Article
Citation

NANO LETTERS, v.19, no.2, pp.839 - 849

ISSN
1530-6984
DOI
10.1021/acs.nanolett.8b04023
URI
http://hdl.handle.net/10203/263601
Appears in Collection
CBE-Journal Papers(저널논문)EE-Journal Papers(저널논문)
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