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An ultrasmall organic synapse for neuromorphic computing
High‐performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain‐inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667342/ https://www.ncbi.nlm.nih.gov/pubmed/37996491 http://dx.doi.org/10.1038/s41467-023-43542-2 |
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author | Liu, Shuzhi Zeng, Jianmin Wu, Zhixin Hu, Han Xu, Ao Huang, Xiaohe Chen, Weilin Chen, Qilai Yu, Zhe Zhao, Yinyu Wang, Rong Han, Tingting Li, Chao Gao, Pingqi Kim, Hyunwoo Baik, Seung Jae Zhang, Ruoyu Zhang, Zhang Zhou, Peng Liu, Gang |
author_facet | Liu, Shuzhi Zeng, Jianmin Wu, Zhixin Hu, Han Xu, Ao Huang, Xiaohe Chen, Weilin Chen, Qilai Yu, Zhe Zhao, Yinyu Wang, Rong Han, Tingting Li, Chao Gao, Pingqi Kim, Hyunwoo Baik, Seung Jae Zhang, Ruoyu Zhang, Zhang Zhou, Peng Liu, Gang |
author_sort | Liu, Shuzhi |
collection | PubMed |
description | High‐performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain‐inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high‐density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL(10) with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as‐fabricated PBFCL(10) synapses can switch between 32 conductance states linearly with a high cycle‐to‐cycle uniformity of 98.89% and device‐to‐device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking‐plasticity‐related algorithm for decision-making tasks. |
format | Online Article Text |
id | pubmed-10667342 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106673422023-11-23 An ultrasmall organic synapse for neuromorphic computing Liu, Shuzhi Zeng, Jianmin Wu, Zhixin Hu, Han Xu, Ao Huang, Xiaohe Chen, Weilin Chen, Qilai Yu, Zhe Zhao, Yinyu Wang, Rong Han, Tingting Li, Chao Gao, Pingqi Kim, Hyunwoo Baik, Seung Jae Zhang, Ruoyu Zhang, Zhang Zhou, Peng Liu, Gang Nat Commun Article High‐performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain‐inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high‐density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL(10) with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as‐fabricated PBFCL(10) synapses can switch between 32 conductance states linearly with a high cycle‐to‐cycle uniformity of 98.89% and device‐to‐device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking‐plasticity‐related algorithm for decision-making tasks. Nature Publishing Group UK 2023-11-23 /pmc/articles/PMC10667342/ /pubmed/37996491 http://dx.doi.org/10.1038/s41467-023-43542-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Liu, Shuzhi Zeng, Jianmin Wu, Zhixin Hu, Han Xu, Ao Huang, Xiaohe Chen, Weilin Chen, Qilai Yu, Zhe Zhao, Yinyu Wang, Rong Han, Tingting Li, Chao Gao, Pingqi Kim, Hyunwoo Baik, Seung Jae Zhang, Ruoyu Zhang, Zhang Zhou, Peng Liu, Gang An ultrasmall organic synapse for neuromorphic computing |
title | An ultrasmall organic synapse for neuromorphic computing |
title_full | An ultrasmall organic synapse for neuromorphic computing |
title_fullStr | An ultrasmall organic synapse for neuromorphic computing |
title_full_unstemmed | An ultrasmall organic synapse for neuromorphic computing |
title_short | An ultrasmall organic synapse for neuromorphic computing |
title_sort | ultrasmall organic synapse for neuromorphic computing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667342/ https://www.ncbi.nlm.nih.gov/pubmed/37996491 http://dx.doi.org/10.1038/s41467-023-43542-2 |
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