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Quantum imaging of the reconfigurable VO(2) synaptic electronics for neuromorphic computing
Neuromorphic computing has shown remarkable capabilities in silicon-based artificial intelligence, which can be optimized by using Mott materials for functional synaptic connections. However, the research efforts focus on two-terminal artificial synapses and envisioned the networks controlled by sil...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10550222/ https://www.ncbi.nlm.nih.gov/pubmed/37792938 http://dx.doi.org/10.1126/sciadv.adg9376 |
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author | Feng, Ce Li, Bo-Wen Dong, Yang Chen, Xiang-Dong Zheng, Yu Wang, Ze-Hao Lin, Hao-Bin Jiang, Wang Zhang, Shao-Chun Zou, Chong-Wen Guo, Guang-Can Sun, Fang-Wen |
author_facet | Feng, Ce Li, Bo-Wen Dong, Yang Chen, Xiang-Dong Zheng, Yu Wang, Ze-Hao Lin, Hao-Bin Jiang, Wang Zhang, Shao-Chun Zou, Chong-Wen Guo, Guang-Can Sun, Fang-Wen |
author_sort | Feng, Ce |
collection | PubMed |
description | Neuromorphic computing has shown remarkable capabilities in silicon-based artificial intelligence, which can be optimized by using Mott materials for functional synaptic connections. However, the research efforts focus on two-terminal artificial synapses and envisioned the networks controlled by silicon-based circuits, which is difficult to develop and integrate. Here, we propose a dynamic network with laser-controlled conducting filaments based on electric field-induced local insulator-metal transition of vanadium dioxide. Quantum sensing is used to realize conductivity-sensitive imaging of conducting filament. We find that the location of filament formation is manipulated by focused laser, which is applicable to simulate the dynamical synaptic connections between the neurons. The ability to process signals with both long-term and short-term potentiation is further demonstrated with ~60 times on/off ratio while switching the pathways. This study opens the door to the development of dynamic network structures depending on easily controlled conduction pathways, mimicking the biological nervous systems. |
format | Online Article Text |
id | pubmed-10550222 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-105502222023-10-05 Quantum imaging of the reconfigurable VO(2) synaptic electronics for neuromorphic computing Feng, Ce Li, Bo-Wen Dong, Yang Chen, Xiang-Dong Zheng, Yu Wang, Ze-Hao Lin, Hao-Bin Jiang, Wang Zhang, Shao-Chun Zou, Chong-Wen Guo, Guang-Can Sun, Fang-Wen Sci Adv Physical and Materials Sciences Neuromorphic computing has shown remarkable capabilities in silicon-based artificial intelligence, which can be optimized by using Mott materials for functional synaptic connections. However, the research efforts focus on two-terminal artificial synapses and envisioned the networks controlled by silicon-based circuits, which is difficult to develop and integrate. Here, we propose a dynamic network with laser-controlled conducting filaments based on electric field-induced local insulator-metal transition of vanadium dioxide. Quantum sensing is used to realize conductivity-sensitive imaging of conducting filament. We find that the location of filament formation is manipulated by focused laser, which is applicable to simulate the dynamical synaptic connections between the neurons. The ability to process signals with both long-term and short-term potentiation is further demonstrated with ~60 times on/off ratio while switching the pathways. This study opens the door to the development of dynamic network structures depending on easily controlled conduction pathways, mimicking the biological nervous systems. American Association for the Advancement of Science 2023-10-04 /pmc/articles/PMC10550222/ /pubmed/37792938 http://dx.doi.org/10.1126/sciadv.adg9376 Text en Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Physical and Materials Sciences Feng, Ce Li, Bo-Wen Dong, Yang Chen, Xiang-Dong Zheng, Yu Wang, Ze-Hao Lin, Hao-Bin Jiang, Wang Zhang, Shao-Chun Zou, Chong-Wen Guo, Guang-Can Sun, Fang-Wen Quantum imaging of the reconfigurable VO(2) synaptic electronics for neuromorphic computing |
title | Quantum imaging of the reconfigurable VO(2) synaptic electronics for neuromorphic computing |
title_full | Quantum imaging of the reconfigurable VO(2) synaptic electronics for neuromorphic computing |
title_fullStr | Quantum imaging of the reconfigurable VO(2) synaptic electronics for neuromorphic computing |
title_full_unstemmed | Quantum imaging of the reconfigurable VO(2) synaptic electronics for neuromorphic computing |
title_short | Quantum imaging of the reconfigurable VO(2) synaptic electronics for neuromorphic computing |
title_sort | quantum imaging of the reconfigurable vo(2) synaptic electronics for neuromorphic computing |
topic | Physical and Materials Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10550222/ https://www.ncbi.nlm.nih.gov/pubmed/37792938 http://dx.doi.org/10.1126/sciadv.adg9376 |
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