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CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing
Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO(2)/TiO(2)/ITO optoelectronic memristor. The de...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640510/ https://www.ncbi.nlm.nih.gov/pubmed/36342556 http://dx.doi.org/10.1186/s11671-022-03744-x |
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author | Wu, Facai Chou, Chien-Hung Tseng, Tseung-Yuen |
author_facet | Wu, Facai Chou, Chien-Hung Tseng, Tseung-Yuen |
author_sort | Wu, Facai |
collection | PubMed |
description | Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO(2)/TiO(2)/ITO optoelectronic memristor. The device shows an average of 79.24% transmittance under visible light. After electroforming, stable bipolar analog switching, data retention beyond 10(4) s, and endurance of 10(6) cycles are realized. An obvious current increase is observed under 405 nm wavelength light irradiation both in high and in low resistance states. The long-term potentiation of synaptic property can be achieved by both electrical and optical stimulation. Moreover, based on the optical potentiation and electrical depression of conductances, the simulated Hopfield neural network (HNN) is trained for learning the 10 × 10 pixels size image. The HNN can be successfully trained to recognize the input image with a training accuracy of 100% in 13 iterations. These results suggest that this optoelectronic memristor has a high potential for neuromorphic application. |
format | Online Article Text |
id | pubmed-9640510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-96405102022-11-15 CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing Wu, Facai Chou, Chien-Hung Tseng, Tseung-Yuen Nanoscale Res Lett Research Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO(2)/TiO(2)/ITO optoelectronic memristor. The device shows an average of 79.24% transmittance under visible light. After electroforming, stable bipolar analog switching, data retention beyond 10(4) s, and endurance of 10(6) cycles are realized. An obvious current increase is observed under 405 nm wavelength light irradiation both in high and in low resistance states. The long-term potentiation of synaptic property can be achieved by both electrical and optical stimulation. Moreover, based on the optical potentiation and electrical depression of conductances, the simulated Hopfield neural network (HNN) is trained for learning the 10 × 10 pixels size image. The HNN can be successfully trained to recognize the input image with a training accuracy of 100% in 13 iterations. These results suggest that this optoelectronic memristor has a high potential for neuromorphic application. Springer US 2022-11-07 /pmc/articles/PMC9640510/ /pubmed/36342556 http://dx.doi.org/10.1186/s11671-022-03744-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Wu, Facai Chou, Chien-Hung Tseng, Tseung-Yuen CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
title | CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
title_full | CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
title_fullStr | CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
title_full_unstemmed | CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
title_short | CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing |
title_sort | cmos-compatible memristor for optoelectronic neuromorphic computing |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640510/ https://www.ncbi.nlm.nih.gov/pubmed/36342556 http://dx.doi.org/10.1186/s11671-022-03744-x |
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