Artificial Neurons Based on Ag/V<sub>2</sub>C/W Threshold Switching Memristors

Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. Howeve...

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Autores principales:	Yu Wang, Xintong Chen, Daqi Shen, Miaocheng Zhang, Xi Chen, Xingyu Chen, Weijing Shao, Hong Gu, Jianguang Xu, Ertao Hu, Lei Wang, Rongqing Xu, Yi Tong
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	MXene memristor threshold switching leaky integrate-and-fire artificial neuron Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/0fd6b26657ad4a6291ab1ab8602fa034
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spelling	oai:doaj.org-article:0fd6b26657ad4a6291ab1ab8602fa0342021-11-25T18:30:25ZArtificial Neurons Based on Ag/V<sub>2</sub>C/W Threshold Switching Memristors10.3390/nano111128602079-4991https://doaj.org/article/0fd6b26657ad4a6291ab1ab8602fa0342021-10-01T00:00:00Zhttps://www.mdpi.com/2079-4991/11/11/2860https://doaj.org/toc/2079-4991Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V<sub>2</sub>C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V<sub>2</sub>C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V<sub>2</sub>C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.Yu WangXintong ChenDaqi ShenMiaocheng ZhangXi ChenXingyu ChenWeijing ShaoHong GuJianguang XuErtao HuLei WangRongqing XuYi TongMDPI AGarticleMXenememristorthreshold switchingleaky integrate-and-fireartificial neuronChemistryQD1-999ENNanomaterials, Vol 11, Iss 2860, p 2860 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MXene memristor threshold switching leaky integrate-and-fire artificial neuron Chemistry QD1-999
spellingShingle	MXene memristor threshold switching leaky integrate-and-fire artificial neuron Chemistry QD1-999 Yu Wang Xintong Chen Daqi Shen Miaocheng Zhang Xi Chen Xingyu Chen Weijing Shao Hong Gu Jianguang Xu Ertao Hu Lei Wang Rongqing Xu Yi Tong Artificial Neurons Based on Ag/V<sub>2</sub>C/W Threshold Switching Memristors
description	Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V<sub>2</sub>C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V<sub>2</sub>C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V<sub>2</sub>C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.
format	article
author	Yu Wang Xintong Chen Daqi Shen Miaocheng Zhang Xi Chen Xingyu Chen Weijing Shao Hong Gu Jianguang Xu Ertao Hu Lei Wang Rongqing Xu Yi Tong
author_facet	Yu Wang Xintong Chen Daqi Shen Miaocheng Zhang Xi Chen Xingyu Chen Weijing Shao Hong Gu Jianguang Xu Ertao Hu Lei Wang Rongqing Xu Yi Tong
author_sort	Yu Wang
title	Artificial Neurons Based on Ag/V<sub>2</sub>C/W Threshold Switching Memristors
title_short	Artificial Neurons Based on Ag/V<sub>2</sub>C/W Threshold Switching Memristors
title_full	Artificial Neurons Based on Ag/V<sub>2</sub>C/W Threshold Switching Memristors
title_fullStr	Artificial Neurons Based on Ag/V<sub>2</sub>C/W Threshold Switching Memristors
title_full_unstemmed	Artificial Neurons Based on Ag/V<sub>2</sub>C/W Threshold Switching Memristors
title_sort	artificial neurons based on ag/v<sub>2</sub>c/w threshold switching memristors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0fd6b26657ad4a6291ab1ab8602fa034
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Artificial Neurons Based on Ag/V<sub>2</sub>C/W Threshold Switching Memristors

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