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Artificial SiN(z):H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing

Inspired by its highly efficient capability to deal with big data, the brain-like computational system has attracted a great amount of attention for its ability to outperform the von Neumann computation paradigm. As the core of the neuromorphic computing chip, an artificial synapse based on the memr...

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Autores principales: Chen, Tong, Ma, Zhongyuan, Hu, Hongsheng, Yang, Yang, Zhou, Chengfeng, Shen, Furao, Xu, Haitao, Xu, Jun, Xu, Ling, Li, Wei, Chen, Kunji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458972/
https://www.ncbi.nlm.nih.gov/pubmed/37630946
http://dx.doi.org/10.3390/nano13162362
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author Chen, Tong
Ma, Zhongyuan
Hu, Hongsheng
Yang, Yang
Zhou, Chengfeng
Shen, Furao
Xu, Haitao
Xu, Jun
Xu, Ling
Li, Wei
Chen, Kunji
author_facet Chen, Tong
Ma, Zhongyuan
Hu, Hongsheng
Yang, Yang
Zhou, Chengfeng
Shen, Furao
Xu, Haitao
Xu, Jun
Xu, Ling
Li, Wei
Chen, Kunji
author_sort Chen, Tong
collection PubMed
description Inspired by its highly efficient capability to deal with big data, the brain-like computational system has attracted a great amount of attention for its ability to outperform the von Neumann computation paradigm. As the core of the neuromorphic computing chip, an artificial synapse based on the memristor, with a high accuracy in processing images, is highly desired. We report, for the first time, that artificial synapse arrays with a high accuracy in image recognition can be obtained through the fabrication of a SiN(z):H memristor with a gradient Si/N ratio. The training accuracy of SiN(z):H synapse arrays for image learning can reach 93.65%. The temperature-dependent I–V characteristic reveals that the gradual Si dangling bond pathway makes the main contribution towards improving the linearity of the tunable conductance. The thinner diameter and fixed disconnection point in the gradual pathway are of benefit in enhancing the accuracy of visual identification. The artificial SiN(z):H synapse arrays display stable and uniform biological functions, such as the short-term biosynaptic functions, including spike-duration-dependent plasticity, spike-number-dependent plasticity, and paired-pulse facilitation, as well as the long-term ones, such as long-term potentiation, long-term depression, and spike-time-dependent plasticity. The highly efficient visual learning capability of the artificial SiN(z):H synapse with a gradual conductive pathway for neuromorphic systems hold great application potential in the age of artificial intelligence (AI).
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spelling pubmed-104589722023-08-27 Artificial SiN(z):H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing Chen, Tong Ma, Zhongyuan Hu, Hongsheng Yang, Yang Zhou, Chengfeng Shen, Furao Xu, Haitao Xu, Jun Xu, Ling Li, Wei Chen, Kunji Nanomaterials (Basel) Article Inspired by its highly efficient capability to deal with big data, the brain-like computational system has attracted a great amount of attention for its ability to outperform the von Neumann computation paradigm. As the core of the neuromorphic computing chip, an artificial synapse based on the memristor, with a high accuracy in processing images, is highly desired. We report, for the first time, that artificial synapse arrays with a high accuracy in image recognition can be obtained through the fabrication of a SiN(z):H memristor with a gradient Si/N ratio. The training accuracy of SiN(z):H synapse arrays for image learning can reach 93.65%. The temperature-dependent I–V characteristic reveals that the gradual Si dangling bond pathway makes the main contribution towards improving the linearity of the tunable conductance. The thinner diameter and fixed disconnection point in the gradual pathway are of benefit in enhancing the accuracy of visual identification. The artificial SiN(z):H synapse arrays display stable and uniform biological functions, such as the short-term biosynaptic functions, including spike-duration-dependent plasticity, spike-number-dependent plasticity, and paired-pulse facilitation, as well as the long-term ones, such as long-term potentiation, long-term depression, and spike-time-dependent plasticity. The highly efficient visual learning capability of the artificial SiN(z):H synapse with a gradual conductive pathway for neuromorphic systems hold great application potential in the age of artificial intelligence (AI). MDPI 2023-08-18 /pmc/articles/PMC10458972/ /pubmed/37630946 http://dx.doi.org/10.3390/nano13162362 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Tong
Ma, Zhongyuan
Hu, Hongsheng
Yang, Yang
Zhou, Chengfeng
Shen, Furao
Xu, Haitao
Xu, Jun
Xu, Ling
Li, Wei
Chen, Kunji
Artificial SiN(z):H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing
title Artificial SiN(z):H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing
title_full Artificial SiN(z):H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing
title_fullStr Artificial SiN(z):H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing
title_full_unstemmed Artificial SiN(z):H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing
title_short Artificial SiN(z):H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing
title_sort artificial sin(z):h synapse crossbar arrays with gradual conductive pathway for high-accuracy neuromorphic computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458972/
https://www.ncbi.nlm.nih.gov/pubmed/37630946
http://dx.doi.org/10.3390/nano13162362
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