ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing

Resistive random-access memory (RRAM) devices have drawn increasing interest for the simplicity of its structure, low power consumption and applicability to neuromorphic computing. By combining analog computing and data storage at the device level, neuromorphic computing system has the potential to...

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Autores principales:	Yifu Huang, Yuqian Gu, Xiaohan Wu, Ruijing Ge, Yao-Feng Chang, Xiyu Wang, Jiahan Zhang, Deji Akinwande, Jack C. Lee
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	RRAM 2D material ReSe2 neuromorphic computing verilog-a artificial neural network Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/9afa5e0c0f2444b793a6d66fb61d8136
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spelling	oai:doaj.org-article:9afa5e0c0f2444b793a6d66fb61d81362021-11-19T11:19:46ZReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing2673-301310.3389/fnano.2021.782836https://doaj.org/article/9afa5e0c0f2444b793a6d66fb61d81362021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnano.2021.782836/fullhttps://doaj.org/toc/2673-3013Resistive random-access memory (RRAM) devices have drawn increasing interest for the simplicity of its structure, low power consumption and applicability to neuromorphic computing. By combining analog computing and data storage at the device level, neuromorphic computing system has the potential to meet the demand of computing power in applications such as artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT). Monolayer rhenium diselenide (ReSe2), as a two-dimensional (2D) material, has been reported to exhibit non-volatile resistive switching (NVRS) behavior in RRAM devices with sub-nanometer active layer thickness. In this paper, we demonstrate stable multiple-step RESET in ReSe2 RRAM devices by applying different levels of DC electrical bias. Pulse measurement has been conducted to study the neuromorphic characteristics. Under different height of stimuli, the ReSe2 RRAM devices have been found to switch to different resistance states, which shows the potentiation of synaptic applications. Long-term potentiation (LTP) and depression (LTD) have been demonstrated with the gradual resistance switching behaviors observed in long-term plasticity programming. A Verilog-A model is proposed based on the multiple-step resistive switching behavior. By implementing the LTP/LTD parameters, an artificial neural network (ANN) is constructed for the demonstration of handwriting classification using Modified National Institute of Standards and Technology (MNIST) dataset.Yifu HuangYuqian GuXiaohan WuRuijing GeYao-Feng ChangXiyu WangJiahan ZhangDeji AkinwandeJack C. LeeFrontiers Media S.A.articleRRAM2D materialReSe2neuromorphic computingverilog-aartificial neural networkChemical technologyTP1-1185ENFrontiers in Nanotechnology, Vol 3 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	RRAM 2D material ReSe2 neuromorphic computing verilog-a artificial neural network Chemical technology TP1-1185
spellingShingle	RRAM 2D material ReSe2 neuromorphic computing verilog-a artificial neural network Chemical technology TP1-1185 Yifu Huang Yuqian Gu Xiaohan Wu Ruijing Ge Yao-Feng Chang Xiyu Wang Jiahan Zhang Deji Akinwande Jack C. Lee ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing
description	Resistive random-access memory (RRAM) devices have drawn increasing interest for the simplicity of its structure, low power consumption and applicability to neuromorphic computing. By combining analog computing and data storage at the device level, neuromorphic computing system has the potential to meet the demand of computing power in applications such as artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT). Monolayer rhenium diselenide (ReSe2), as a two-dimensional (2D) material, has been reported to exhibit non-volatile resistive switching (NVRS) behavior in RRAM devices with sub-nanometer active layer thickness. In this paper, we demonstrate stable multiple-step RESET in ReSe2 RRAM devices by applying different levels of DC electrical bias. Pulse measurement has been conducted to study the neuromorphic characteristics. Under different height of stimuli, the ReSe2 RRAM devices have been found to switch to different resistance states, which shows the potentiation of synaptic applications. Long-term potentiation (LTP) and depression (LTD) have been demonstrated with the gradual resistance switching behaviors observed in long-term plasticity programming. A Verilog-A model is proposed based on the multiple-step resistive switching behavior. By implementing the LTP/LTD parameters, an artificial neural network (ANN) is constructed for the demonstration of handwriting classification using Modified National Institute of Standards and Technology (MNIST) dataset.
format	article
author	Yifu Huang Yuqian Gu Xiaohan Wu Ruijing Ge Yao-Feng Chang Xiyu Wang Jiahan Zhang Deji Akinwande Jack C. Lee
author_facet	Yifu Huang Yuqian Gu Xiaohan Wu Ruijing Ge Yao-Feng Chang Xiyu Wang Jiahan Zhang Deji Akinwande Jack C. Lee
author_sort	Yifu Huang
title	ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing
title_short	ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing
title_full	ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing
title_fullStr	ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing
title_full_unstemmed	ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing
title_sort	rese2-based rram and circuit-level model for neuromorphic computing
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/9afa5e0c0f2444b793a6d66fb61d8136
work_keys_str_mv	AT yifuhuang rese2basedrramandcircuitlevelmodelforneuromorphiccomputing AT yuqiangu rese2basedrramandcircuitlevelmodelforneuromorphiccomputing AT xiaohanwu rese2basedrramandcircuitlevelmodelforneuromorphiccomputing AT ruijingge rese2basedrramandcircuitlevelmodelforneuromorphiccomputing AT yaofengchang rese2basedrramandcircuitlevelmodelforneuromorphiccomputing AT xiyuwang rese2basedrramandcircuitlevelmodelforneuromorphiccomputing AT jiahanzhang rese2basedrramandcircuitlevelmodelforneuromorphiccomputing AT dejiakinwande rese2basedrramandcircuitlevelmodelforneuromorphiccomputing AT jackclee rese2basedrramandcircuitlevelmodelforneuromorphiccomputing
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ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing

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