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Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing
With the advent of the big data era, applications are more data-centric and energy efficiency issues caused by frequent data interactions, due to the physical separation of memory and computing, will become increasingly severe. Emerging technologies have been proposed to perform analog computing wit...
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/PMC7782550/ https://www.ncbi.nlm.nih.gov/pubmed/33397907 http://dx.doi.org/10.1038/s41467-020-20257-2 |
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author | Wang, Shuiyuan Liu, Lan Gan, Lurong Chen, Huawei Hou, Xiang Ding, Yi Ma, Shunli Zhang, David Wei Zhou, Peng |
author_facet | Wang, Shuiyuan Liu, Lan Gan, Lurong Chen, Huawei Hou, Xiang Ding, Yi Ma, Shunli Zhang, David Wei Zhou, Peng |
author_sort | Wang, Shuiyuan |
collection | PubMed |
description | With the advent of the big data era, applications are more data-centric and energy efficiency issues caused by frequent data interactions, due to the physical separation of memory and computing, will become increasingly severe. Emerging technologies have been proposed to perform analog computing with memory to address the dilemma. Ferroelectric memory has become a promising technology due to field-driven fast switching and non-destructive readout, but endurance and miniaturization are limited. Here, we demonstrate the α-In(2)Se(3) ferroelectric semiconductor channel device that integrates non-volatile memory and neural computation functions. Remarkable performance includes ultra-fast write speed of 40 ns, improved endurance through the internal electric field, flexible adjustment of neural plasticity, ultra-low energy consumption of 234/40 fJ per event for excitation/inhibition, and thermally modulated 94.74% high-precision iris recognition classification simulation. This prototypical demonstration lays the foundation for an integrated memory computing system with high density and energy efficiency. |
format | Online Article Text |
id | pubmed-7782550 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77825502021-01-11 Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing Wang, Shuiyuan Liu, Lan Gan, Lurong Chen, Huawei Hou, Xiang Ding, Yi Ma, Shunli Zhang, David Wei Zhou, Peng Nat Commun Article With the advent of the big data era, applications are more data-centric and energy efficiency issues caused by frequent data interactions, due to the physical separation of memory and computing, will become increasingly severe. Emerging technologies have been proposed to perform analog computing with memory to address the dilemma. Ferroelectric memory has become a promising technology due to field-driven fast switching and non-destructive readout, but endurance and miniaturization are limited. Here, we demonstrate the α-In(2)Se(3) ferroelectric semiconductor channel device that integrates non-volatile memory and neural computation functions. Remarkable performance includes ultra-fast write speed of 40 ns, improved endurance through the internal electric field, flexible adjustment of neural plasticity, ultra-low energy consumption of 234/40 fJ per event for excitation/inhibition, and thermally modulated 94.74% high-precision iris recognition classification simulation. This prototypical demonstration lays the foundation for an integrated memory computing system with high density and energy efficiency. Nature Publishing Group UK 2021-01-04 /pmc/articles/PMC7782550/ /pubmed/33397907 http://dx.doi.org/10.1038/s41467-020-20257-2 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 Wang, Shuiyuan Liu, Lan Gan, Lurong Chen, Huawei Hou, Xiang Ding, Yi Ma, Shunli Zhang, David Wei Zhou, Peng Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing |
title | Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing |
title_full | Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing |
title_fullStr | Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing |
title_full_unstemmed | Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing |
title_short | Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing |
title_sort | two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7782550/ https://www.ncbi.nlm.nih.gov/pubmed/33397907 http://dx.doi.org/10.1038/s41467-020-20257-2 |
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