Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing

Abstract The development of brain-inspired neuromorphic computing, including artificial intelligence (AI) and machine learning, is of considerable importance because of the rapid growth in hardware and software capacities, which allows for the efficient handling of big data. Devices for neuromorphic...

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Autores principales:	Tae-Ju Lee, Su-Kyung Kim, Tae-Yeon Seong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ad19b692bb8c458291342b9439dc49a8
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spelling	oai:doaj.org-article:ad19b692bb8c458291342b9439dc49a82021-12-02T18:18:07ZSputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing10.1038/s41598-020-62642-32045-2322https://doaj.org/article/ad19b692bb8c458291342b9439dc49a82020-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-62642-3https://doaj.org/toc/2045-2322Abstract The development of brain-inspired neuromorphic computing, including artificial intelligence (AI) and machine learning, is of considerable importance because of the rapid growth in hardware and software capacities, which allows for the efficient handling of big data. Devices for neuromorphic computing must satisfy basic requirements such as multilevel states, high operating speeds, low energy consumption, and sufficient endurance, retention and linearity. In this study, inorganic perovskite-type amorphous strontium vanadate (a-SrVOx: a-SVO) synthesized at room temperature is utilized to produce a high-performance memristor that demonstrates nonvolatile multilevel resistive switching and synaptic characteristics. Analysis of the electrical characteristics indicates that the a-SVO memristor illustrates typical bipolar resistive switching behavior. Multilevel resistance states are also observed in the off-to-on and on-to-off transition processes. The retention resistance of the a-SVO memristor is shown to not significantly change for a period of 2 × 104 s. The conduction mechanism operating within the Ag/a-SVO/Pt memristor is ascribed to the formation of Ag-based filaments. Nonlinear neural network simulations are also conducted to evaluate the synaptic behavior. These results demonstrate that a-SVO-based memristors hold great promise for use in high-performance neuromorphic computing devices.Tae-Ju LeeSu-Kyung KimTae-Yeon SeongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tae-Ju Lee Su-Kyung Kim Tae-Yeon Seong Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing
description	Abstract The development of brain-inspired neuromorphic computing, including artificial intelligence (AI) and machine learning, is of considerable importance because of the rapid growth in hardware and software capacities, which allows for the efficient handling of big data. Devices for neuromorphic computing must satisfy basic requirements such as multilevel states, high operating speeds, low energy consumption, and sufficient endurance, retention and linearity. In this study, inorganic perovskite-type amorphous strontium vanadate (a-SrVOx: a-SVO) synthesized at room temperature is utilized to produce a high-performance memristor that demonstrates nonvolatile multilevel resistive switching and synaptic characteristics. Analysis of the electrical characteristics indicates that the a-SVO memristor illustrates typical bipolar resistive switching behavior. Multilevel resistance states are also observed in the off-to-on and on-to-off transition processes. The retention resistance of the a-SVO memristor is shown to not significantly change for a period of 2 × 104 s. The conduction mechanism operating within the Ag/a-SVO/Pt memristor is ascribed to the formation of Ag-based filaments. Nonlinear neural network simulations are also conducted to evaluate the synaptic behavior. These results demonstrate that a-SVO-based memristors hold great promise for use in high-performance neuromorphic computing devices.
format	article
author	Tae-Ju Lee Su-Kyung Kim Tae-Yeon Seong
author_facet	Tae-Ju Lee Su-Kyung Kim Tae-Yeon Seong
author_sort	Tae-Ju Lee
title	Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing
title_short	Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing
title_full	Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing
title_fullStr	Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing
title_full_unstemmed	Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing
title_sort	sputtering-deposited amorphous srvox-based memristor for use in neuromorphic computing
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ad19b692bb8c458291342b9439dc49a8
work_keys_str_mv	AT taejulee sputteringdepositedamorphoussrvoxbasedmemristorforuseinneuromorphiccomputing AT sukyungkim sputteringdepositedamorphoussrvoxbasedmemristorforuseinneuromorphiccomputing AT taeyeonseong sputteringdepositedamorphoussrvoxbasedmemristorforuseinneuromorphiccomputing
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Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing

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