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Neuromorphic device based on silicon nanosheets
Silicon is vital for its high abundance, vast production, and perfect compatibility with the well-established CMOS processing industry. Recently, artificially stacked layered 2D structures have gained tremendous attention via fine-tuning properties for electronic devices. This article presents neuro...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445003/ https://www.ncbi.nlm.nih.gov/pubmed/36064545 http://dx.doi.org/10.1038/s41467-022-32884-y |
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author | Wang, Chenhao Xu, Xinyi Pi, Xiaodong Butala, Mark D. Huang, Wen Yin, Lei Peng, Wenbing Ali, Munir Bodepudi, Srikrishna Chanakya Qiao, Xvsheng Xu, Yang Sun, Wei Yang, Deren |
author_facet | Wang, Chenhao Xu, Xinyi Pi, Xiaodong Butala, Mark D. Huang, Wen Yin, Lei Peng, Wenbing Ali, Munir Bodepudi, Srikrishna Chanakya Qiao, Xvsheng Xu, Yang Sun, Wei Yang, Deren |
author_sort | Wang, Chenhao |
collection | PubMed |
description | Silicon is vital for its high abundance, vast production, and perfect compatibility with the well-established CMOS processing industry. Recently, artificially stacked layered 2D structures have gained tremendous attention via fine-tuning properties for electronic devices. This article presents neuromorphic devices based on silicon nanosheets that are chemically exfoliated and surface-modified, enabling self-assembly into hierarchical stacking structures. The device functionality can be switched between a unipolar memristor and a feasibly reset-able synaptic device. The memory function of the device is based on the charge storage in the partially oxidized SiNS stacks followed by the discharge activated by the electric field at the Au-Si Schottky interface, as verified in both experimental and theoretical means. This work further inspired elegant neuromorphic computation models for digit recognition and noise filtration. Ultimately, it brings silicon - the most established semiconductor - back to the forefront for next-generation computations. |
format | Online Article Text |
id | pubmed-9445003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-94450032022-09-07 Neuromorphic device based on silicon nanosheets Wang, Chenhao Xu, Xinyi Pi, Xiaodong Butala, Mark D. Huang, Wen Yin, Lei Peng, Wenbing Ali, Munir Bodepudi, Srikrishna Chanakya Qiao, Xvsheng Xu, Yang Sun, Wei Yang, Deren Nat Commun Article Silicon is vital for its high abundance, vast production, and perfect compatibility with the well-established CMOS processing industry. Recently, artificially stacked layered 2D structures have gained tremendous attention via fine-tuning properties for electronic devices. This article presents neuromorphic devices based on silicon nanosheets that are chemically exfoliated and surface-modified, enabling self-assembly into hierarchical stacking structures. The device functionality can be switched between a unipolar memristor and a feasibly reset-able synaptic device. The memory function of the device is based on the charge storage in the partially oxidized SiNS stacks followed by the discharge activated by the electric field at the Au-Si Schottky interface, as verified in both experimental and theoretical means. This work further inspired elegant neuromorphic computation models for digit recognition and noise filtration. Ultimately, it brings silicon - the most established semiconductor - back to the forefront for next-generation computations. Nature Publishing Group UK 2022-09-05 /pmc/articles/PMC9445003/ /pubmed/36064545 http://dx.doi.org/10.1038/s41467-022-32884-y Text en © The Author(s) 2022 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 Wang, Chenhao Xu, Xinyi Pi, Xiaodong Butala, Mark D. Huang, Wen Yin, Lei Peng, Wenbing Ali, Munir Bodepudi, Srikrishna Chanakya Qiao, Xvsheng Xu, Yang Sun, Wei Yang, Deren Neuromorphic device based on silicon nanosheets |
title | Neuromorphic device based on silicon nanosheets |
title_full | Neuromorphic device based on silicon nanosheets |
title_fullStr | Neuromorphic device based on silicon nanosheets |
title_full_unstemmed | Neuromorphic device based on silicon nanosheets |
title_short | Neuromorphic device based on silicon nanosheets |
title_sort | neuromorphic device based on silicon nanosheets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445003/ https://www.ncbi.nlm.nih.gov/pubmed/36064545 http://dx.doi.org/10.1038/s41467-022-32884-y |
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