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High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network
Spintronic devices are considered as one of the most promising technologies for non‐volatile memory and computing. However, two crucial drawbacks, that is, lack of intrinsic multi‐level operation and low on/off ratio, greatly hinder their further application for advanced computing concepts, such as...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069383/ https://www.ncbi.nlm.nih.gov/pubmed/35229495 http://dx.doi.org/10.1002/advs.202103357 |
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author | Zhang, Kun Jia, Xiaotao Cao, Kaihua Wang, Jinkai Zhang, Yue Lin, Kelian Chen, Lei Feng, Xueqiang Zheng, Zhenyi Zhang, Zhizhong Zhang, Youguang Zhao, Weisheng |
author_facet | Zhang, Kun Jia, Xiaotao Cao, Kaihua Wang, Jinkai Zhang, Yue Lin, Kelian Chen, Lei Feng, Xueqiang Zheng, Zhenyi Zhang, Zhizhong Zhang, Youguang Zhao, Weisheng |
author_sort | Zhang, Kun |
collection | PubMed |
description | Spintronic devices are considered as one of the most promising technologies for non‐volatile memory and computing. However, two crucial drawbacks, that is, lack of intrinsic multi‐level operation and low on/off ratio, greatly hinder their further application for advanced computing concepts, such as deep neural network (DNN) accelerator. In this paper, a spintronic multi‐level memory unit with high on/off ratio is proposed by integrating several series‐connected magnetic tunnel junctions (MTJs) with perpendicular magnetic anisotropy (PMA) and a Schottky diode in parallel. Due to the rectification effect on the PMA MTJ, an on/off ratio over 100, two orders of magnitude higher than intrinsic values, is obtained under proper proportion of alternating current and direct current. Multiple resistance states are stably achieved and can be reconfigured by spin transfer torque effect. A computing‐in‐memory architecture based DNN accelerator for image classification with the experimental parameters of this proposal to evidence its application potential is also evaluated. This work can satisfy the rigorous requirements of DNN for memory unit and promote the development of high‐accuracy and robust artificial intelligence applications. |
format | Online Article Text |
id | pubmed-9069383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90693832022-05-09 High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network Zhang, Kun Jia, Xiaotao Cao, Kaihua Wang, Jinkai Zhang, Yue Lin, Kelian Chen, Lei Feng, Xueqiang Zheng, Zhenyi Zhang, Zhizhong Zhang, Youguang Zhao, Weisheng Adv Sci (Weinh) Research Articles Spintronic devices are considered as one of the most promising technologies for non‐volatile memory and computing. However, two crucial drawbacks, that is, lack of intrinsic multi‐level operation and low on/off ratio, greatly hinder their further application for advanced computing concepts, such as deep neural network (DNN) accelerator. In this paper, a spintronic multi‐level memory unit with high on/off ratio is proposed by integrating several series‐connected magnetic tunnel junctions (MTJs) with perpendicular magnetic anisotropy (PMA) and a Schottky diode in parallel. Due to the rectification effect on the PMA MTJ, an on/off ratio over 100, two orders of magnitude higher than intrinsic values, is obtained under proper proportion of alternating current and direct current. Multiple resistance states are stably achieved and can be reconfigured by spin transfer torque effect. A computing‐in‐memory architecture based DNN accelerator for image classification with the experimental parameters of this proposal to evidence its application potential is also evaluated. This work can satisfy the rigorous requirements of DNN for memory unit and promote the development of high‐accuracy and robust artificial intelligence applications. John Wiley and Sons Inc. 2022-02-20 /pmc/articles/PMC9069383/ /pubmed/35229495 http://dx.doi.org/10.1002/advs.202103357 Text en © 2022 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Zhang, Kun Jia, Xiaotao Cao, Kaihua Wang, Jinkai Zhang, Yue Lin, Kelian Chen, Lei Feng, Xueqiang Zheng, Zhenyi Zhang, Zhizhong Zhang, Youguang Zhao, Weisheng High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network |
title | High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network |
title_full | High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network |
title_fullStr | High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network |
title_full_unstemmed | High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network |
title_short | High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network |
title_sort | high on/off ratio spintronic multi‐level memory unit for deep neural network |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069383/ https://www.ncbi.nlm.nih.gov/pubmed/35229495 http://dx.doi.org/10.1002/advs.202103357 |
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