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Compact artificial neuron based on anti-ferroelectric transistor
Neuromorphic machines are intriguing for building energy-efficient intelligent systems, where spiking neurons are pivotal components. Recently, memristive neurons with promising bio-plausibility have been developed, but with limited reliability, bulky capacitors or additional reset circuits. Here, w...
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/PMC9668812/ https://www.ncbi.nlm.nih.gov/pubmed/36384960 http://dx.doi.org/10.1038/s41467-022-34774-9 |
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author | Cao, Rongrong Zhang, Xumeng Liu, Sen Lu, Jikai Wang, Yongzhou Jiang, Hao Yang, Yang Sun, Yize Wei, Wei Wang, Jianlu Xu, Hui Li, Qingjiang Liu, Qi |
author_facet | Cao, Rongrong Zhang, Xumeng Liu, Sen Lu, Jikai Wang, Yongzhou Jiang, Hao Yang, Yang Sun, Yize Wei, Wei Wang, Jianlu Xu, Hui Li, Qingjiang Liu, Qi |
author_sort | Cao, Rongrong |
collection | PubMed |
description | Neuromorphic machines are intriguing for building energy-efficient intelligent systems, where spiking neurons are pivotal components. Recently, memristive neurons with promising bio-plausibility have been developed, but with limited reliability, bulky capacitors or additional reset circuits. Here, we propose an anti-ferroelectric field-effect transistor neuron based on the inherent polarization and depolarization of Hf(0.2)Zr(0.8)O(2) anti-ferroelectric film to meet these challenges. The intrinsic accumulated polarization/spontaneous depolarization of Hf(0.2)Zr(0.8)O(2) films implements the integration/leaky behavior of neurons, avoiding external capacitors and reset circuits. Moreover, the anti-ferroelectric neuron exhibits low energy consumption (37 fJ/spike), high endurance (>10(12)), high uniformity and high stability. We further construct a two-layer fully ferroelectric spiking neural networks that combines anti-ferroelectric neurons and ferroelectric synapses, achieving 96.8% recognition accuracy on the Modified National Institute of Standards and Technology dataset. This work opens the way to emulate neurons with anti-ferroelectric materials and provides a promising approach to building high-efficient neuromorphic hardware. |
format | Online Article Text |
id | pubmed-9668812 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96688122022-11-18 Compact artificial neuron based on anti-ferroelectric transistor Cao, Rongrong Zhang, Xumeng Liu, Sen Lu, Jikai Wang, Yongzhou Jiang, Hao Yang, Yang Sun, Yize Wei, Wei Wang, Jianlu Xu, Hui Li, Qingjiang Liu, Qi Nat Commun Article Neuromorphic machines are intriguing for building energy-efficient intelligent systems, where spiking neurons are pivotal components. Recently, memristive neurons with promising bio-plausibility have been developed, but with limited reliability, bulky capacitors or additional reset circuits. Here, we propose an anti-ferroelectric field-effect transistor neuron based on the inherent polarization and depolarization of Hf(0.2)Zr(0.8)O(2) anti-ferroelectric film to meet these challenges. The intrinsic accumulated polarization/spontaneous depolarization of Hf(0.2)Zr(0.8)O(2) films implements the integration/leaky behavior of neurons, avoiding external capacitors and reset circuits. Moreover, the anti-ferroelectric neuron exhibits low energy consumption (37 fJ/spike), high endurance (>10(12)), high uniformity and high stability. We further construct a two-layer fully ferroelectric spiking neural networks that combines anti-ferroelectric neurons and ferroelectric synapses, achieving 96.8% recognition accuracy on the Modified National Institute of Standards and Technology dataset. This work opens the way to emulate neurons with anti-ferroelectric materials and provides a promising approach to building high-efficient neuromorphic hardware. Nature Publishing Group UK 2022-11-17 /pmc/articles/PMC9668812/ /pubmed/36384960 http://dx.doi.org/10.1038/s41467-022-34774-9 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 Cao, Rongrong Zhang, Xumeng Liu, Sen Lu, Jikai Wang, Yongzhou Jiang, Hao Yang, Yang Sun, Yize Wei, Wei Wang, Jianlu Xu, Hui Li, Qingjiang Liu, Qi Compact artificial neuron based on anti-ferroelectric transistor |
title | Compact artificial neuron based on anti-ferroelectric transistor |
title_full | Compact artificial neuron based on anti-ferroelectric transistor |
title_fullStr | Compact artificial neuron based on anti-ferroelectric transistor |
title_full_unstemmed | Compact artificial neuron based on anti-ferroelectric transistor |
title_short | Compact artificial neuron based on anti-ferroelectric transistor |
title_sort | compact artificial neuron based on anti-ferroelectric transistor |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668812/ https://www.ncbi.nlm.nih.gov/pubmed/36384960 http://dx.doi.org/10.1038/s41467-022-34774-9 |
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