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90% yield production of polymer nano-memristor for in-memory computing
Polymer memristors with light weight and mechanical flexibility are preeminent candidates for low-power edge computing paradigms. However, the structural inhomogeneity of most polymers usually leads to random resistive switching characteristics, which lowers the production yield and reliability of n...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012610/ https://www.ncbi.nlm.nih.gov/pubmed/33790277 http://dx.doi.org/10.1038/s41467-021-22243-8 |
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author | Zhang, Bin Chen, Weilin Zeng, Jianmin Fan, Fei Gu, Junwei Chen, Xinhui Yan, Lin Xie, Guangjun Liu, Shuzhi Yan, Qing Baik, Seung Jae Zhang, Zhi-Guo Chen, Weihua Hou, Jie El-Khouly, Mohamed E. Zhang, Zhang Liu, Gang Chen, Yu |
author_facet | Zhang, Bin Chen, Weilin Zeng, Jianmin Fan, Fei Gu, Junwei Chen, Xinhui Yan, Lin Xie, Guangjun Liu, Shuzhi Yan, Qing Baik, Seung Jae Zhang, Zhi-Guo Chen, Weihua Hou, Jie El-Khouly, Mohamed E. Zhang, Zhang Liu, Gang Chen, Yu |
author_sort | Zhang, Bin |
collection | PubMed |
description | Polymer memristors with light weight and mechanical flexibility are preeminent candidates for low-power edge computing paradigms. However, the structural inhomogeneity of most polymers usually leads to random resistive switching characteristics, which lowers the production yield and reliability of nanoscale devices. In this contribution, we report that by adopting the two-dimensional conjugation strategy, a record high 90% production yield of polymer memristors has been achieved with miniaturization and low power potentials. By constructing coplanar macromolecules with 2D conjugated thiophene derivatives to enhance the π–π stacking and crystallinity of the thin film, homogeneous switching takes place across the entire polymer layer, with fast responses in 32 ns, D2D variation down to 3.16% ~ 8.29%, production yield approaching 90%, and scalability into 100 nm scale with tiny power consumption of ~ 10(−15) J/bit. The polymer memristor array is capable of acting as both the arithmetic-logic element and multiply-accumulate accelerator for neuromorphic computing tasks. |
format | Online Article Text |
id | pubmed-8012610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80126102021-04-16 90% yield production of polymer nano-memristor for in-memory computing Zhang, Bin Chen, Weilin Zeng, Jianmin Fan, Fei Gu, Junwei Chen, Xinhui Yan, Lin Xie, Guangjun Liu, Shuzhi Yan, Qing Baik, Seung Jae Zhang, Zhi-Guo Chen, Weihua Hou, Jie El-Khouly, Mohamed E. Zhang, Zhang Liu, Gang Chen, Yu Nat Commun Article Polymer memristors with light weight and mechanical flexibility are preeminent candidates for low-power edge computing paradigms. However, the structural inhomogeneity of most polymers usually leads to random resistive switching characteristics, which lowers the production yield and reliability of nanoscale devices. In this contribution, we report that by adopting the two-dimensional conjugation strategy, a record high 90% production yield of polymer memristors has been achieved with miniaturization and low power potentials. By constructing coplanar macromolecules with 2D conjugated thiophene derivatives to enhance the π–π stacking and crystallinity of the thin film, homogeneous switching takes place across the entire polymer layer, with fast responses in 32 ns, D2D variation down to 3.16% ~ 8.29%, production yield approaching 90%, and scalability into 100 nm scale with tiny power consumption of ~ 10(−15) J/bit. The polymer memristor array is capable of acting as both the arithmetic-logic element and multiply-accumulate accelerator for neuromorphic computing tasks. Nature Publishing Group UK 2021-03-31 /pmc/articles/PMC8012610/ /pubmed/33790277 http://dx.doi.org/10.1038/s41467-021-22243-8 Text en © The Author(s) 2021 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/. |
spellingShingle | Article Zhang, Bin Chen, Weilin Zeng, Jianmin Fan, Fei Gu, Junwei Chen, Xinhui Yan, Lin Xie, Guangjun Liu, Shuzhi Yan, Qing Baik, Seung Jae Zhang, Zhi-Guo Chen, Weihua Hou, Jie El-Khouly, Mohamed E. Zhang, Zhang Liu, Gang Chen, Yu 90% yield production of polymer nano-memristor for in-memory computing |
title | 90% yield production of polymer nano-memristor for in-memory computing |
title_full | 90% yield production of polymer nano-memristor for in-memory computing |
title_fullStr | 90% yield production of polymer nano-memristor for in-memory computing |
title_full_unstemmed | 90% yield production of polymer nano-memristor for in-memory computing |
title_short | 90% yield production of polymer nano-memristor for in-memory computing |
title_sort | 90% yield production of polymer nano-memristor for in-memory computing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012610/ https://www.ncbi.nlm.nih.gov/pubmed/33790277 http://dx.doi.org/10.1038/s41467-021-22243-8 |
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