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Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification

Selective attention is an efficient processing strategy to allocate computational resources for pivotal optical information. However, the hardware implementation of selective visual attention in conventional intelligent system is usually bulky and complex along with high computational cost. Here, pr...

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Autores principales: Yu, Rengjian, He, Lihua, Gao, Changsong, Zhang, Xianghong, Li, Enlong, Guo, Tailiang, Li, Wenwu, Chen, Huipeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669032/
https://www.ncbi.nlm.nih.gov/pubmed/36384983
http://dx.doi.org/10.1038/s41467-022-34565-2
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author Yu, Rengjian
He, Lihua
Gao, Changsong
Zhang, Xianghong
Li, Enlong
Guo, Tailiang
Li, Wenwu
Chen, Huipeng
author_facet Yu, Rengjian
He, Lihua
Gao, Changsong
Zhang, Xianghong
Li, Enlong
Guo, Tailiang
Li, Wenwu
Chen, Huipeng
author_sort Yu, Rengjian
collection PubMed
description Selective attention is an efficient processing strategy to allocate computational resources for pivotal optical information. However, the hardware implementation of selective visual attention in conventional intelligent system is usually bulky and complex along with high computational cost. Here, programmable ferroelectric bionic vision hardware to emulate the selective attention is proposed. The tunneling effect of photogenerated carriers are controlled by dynamic variation of energy barrier, enabling the modulation of memory strength from 9.1% to 47.1% without peripheral storage unit. The molecular polarization of ferroelectric P(VDF-TrFE) layer enables a single device not only multiple nonvolatile states but also the implementation of selective attention. With these ferroelectric devices are arrayed together, UV light information can be selectively recorded and suppressed the with high current decibel level. Furthermore, the device with positive polarization exhibits high wavelength dependence in the image attention processing, and the fabricated ferroelectric sensory network exhibits high accuracy of 95.7% in the pattern classification for multi-wavelength images. This study can enrich the neuromorphic functions of bioinspired sensing devices and pave the way for profound implications of future bioinspired optoelectronics.
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spelling pubmed-96690322022-11-18 Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification Yu, Rengjian He, Lihua Gao, Changsong Zhang, Xianghong Li, Enlong Guo, Tailiang Li, Wenwu Chen, Huipeng Nat Commun Article Selective attention is an efficient processing strategy to allocate computational resources for pivotal optical information. However, the hardware implementation of selective visual attention in conventional intelligent system is usually bulky and complex along with high computational cost. Here, programmable ferroelectric bionic vision hardware to emulate the selective attention is proposed. The tunneling effect of photogenerated carriers are controlled by dynamic variation of energy barrier, enabling the modulation of memory strength from 9.1% to 47.1% without peripheral storage unit. The molecular polarization of ferroelectric P(VDF-TrFE) layer enables a single device not only multiple nonvolatile states but also the implementation of selective attention. With these ferroelectric devices are arrayed together, UV light information can be selectively recorded and suppressed the with high current decibel level. Furthermore, the device with positive polarization exhibits high wavelength dependence in the image attention processing, and the fabricated ferroelectric sensory network exhibits high accuracy of 95.7% in the pattern classification for multi-wavelength images. This study can enrich the neuromorphic functions of bioinspired sensing devices and pave the way for profound implications of future bioinspired optoelectronics. Nature Publishing Group UK 2022-11-17 /pmc/articles/PMC9669032/ /pubmed/36384983 http://dx.doi.org/10.1038/s41467-022-34565-2 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
Yu, Rengjian
He, Lihua
Gao, Changsong
Zhang, Xianghong
Li, Enlong
Guo, Tailiang
Li, Wenwu
Chen, Huipeng
Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification
title Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification
title_full Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification
title_fullStr Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification
title_full_unstemmed Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification
title_short Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification
title_sort programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669032/
https://www.ncbi.nlm.nih.gov/pubmed/36384983
http://dx.doi.org/10.1038/s41467-022-34565-2
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