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Photo-induced non-volatile VO 2 phase transition for neuromorphic ultraviolet sensors
In the quest for emerging in-sensor computing, materials that respond to optical stimuli in conjunction with non-volatile phase transition are highly desired for realizing bioinspired neuromorphic vision components. Here, we report a non-volatile multi-level control of VO films by oxygen stoichiomet...
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Published in: | Nature communications 2022-04, Vol.13 (1), p.1729 |
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container_title | Nature communications |
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creator | Li, Ge Xie, Donggang Zhong, Hai Zhang, Ziye Fu, Xingke Zhou, Qingli Li, Qiang Ni, Hao Wang, Jiaou Guo, Er-Jia He, Meng Wang, Can Yang, Guozhen Jin, Kuijuan Ge, Chen |
description | In the quest for emerging in-sensor computing, materials that respond to optical stimuli in conjunction with non-volatile phase transition are highly desired for realizing bioinspired neuromorphic vision components. Here, we report a non-volatile multi-level control of VO
films by oxygen stoichiometry engineering under ultraviolet irradiation. Based on the reversible regulation of VO
films using ultraviolet irradiation and electrolyte gating, we demonstrate a proof-of-principle neuromorphic ultraviolet sensor with integrated sensing, memory, and processing functions at room temperature, and also prove its silicon compatible potential through the wafer-scale integration of a neuromorphic sensor array. The device displays linear weight update with optical writing because its metallic phase proportion increases almost linearly with the light dosage. Moreover, the artificial neural network consisting of this neuromorphic sensor can extract ultraviolet information from the surrounding environment, and significantly improve the recognition accuracy from 24% to 93%. This work provides a path to design neuromorphic sensors and will facilitate the potential applications in artificial vision systems. |
doi_str_mv | 10.1038/s41467-022-29456-5 |
format | article |
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films using ultraviolet irradiation and electrolyte gating, we demonstrate a proof-of-principle neuromorphic ultraviolet sensor with integrated sensing, memory, and processing functions at room temperature, and also prove its silicon compatible potential through the wafer-scale integration of a neuromorphic sensor array. The device displays linear weight update with optical writing because its metallic phase proportion increases almost linearly with the light dosage. Moreover, the artificial neural network consisting of this neuromorphic sensor can extract ultraviolet information from the surrounding environment, and significantly improve the recognition accuracy from 24% to 93%. This work provides a path to design neuromorphic sensors and will facilitate the potential applications in artificial vision systems.</description><identifier>EISSN: 2041-1723</identifier><identifier>DOI: 10.1038/s41467-022-29456-5</identifier><identifier>PMID: 35365642</identifier><language>eng</language><publisher>England</publisher><subject>Neural Networks, Computer ; Phase Transition ; Vision, Ocular</subject><ispartof>Nature communications, 2022-04, Vol.13 (1), p.1729</ispartof><rights>2022. The Author(s).</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-4404-7957 ; 0000-0001-5702-225X ; 0000-0002-8093-940X ; 0000-0001-8891-260X ; 0000-0002-0047-4375</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27915,27916</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35365642$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Ge</creatorcontrib><creatorcontrib>Xie, Donggang</creatorcontrib><creatorcontrib>Zhong, Hai</creatorcontrib><creatorcontrib>Zhang, Ziye</creatorcontrib><creatorcontrib>Fu, Xingke</creatorcontrib><creatorcontrib>Zhou, Qingli</creatorcontrib><creatorcontrib>Li, Qiang</creatorcontrib><creatorcontrib>Ni, Hao</creatorcontrib><creatorcontrib>Wang, Jiaou</creatorcontrib><creatorcontrib>Guo, Er-Jia</creatorcontrib><creatorcontrib>He, Meng</creatorcontrib><creatorcontrib>Wang, Can</creatorcontrib><creatorcontrib>Yang, Guozhen</creatorcontrib><creatorcontrib>Jin, Kuijuan</creatorcontrib><creatorcontrib>Ge, Chen</creatorcontrib><title>Photo-induced non-volatile VO 2 phase transition for neuromorphic ultraviolet sensors</title><title>Nature communications</title><addtitle>Nat Commun</addtitle><description>In the quest for emerging in-sensor computing, materials that respond to optical stimuli in conjunction with non-volatile phase transition are highly desired for realizing bioinspired neuromorphic vision components. Here, we report a non-volatile multi-level control of VO
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films using ultraviolet irradiation and electrolyte gating, we demonstrate a proof-of-principle neuromorphic ultraviolet sensor with integrated sensing, memory, and processing functions at room temperature, and also prove its silicon compatible potential through the wafer-scale integration of a neuromorphic sensor array. The device displays linear weight update with optical writing because its metallic phase proportion increases almost linearly with the light dosage. Moreover, the artificial neural network consisting of this neuromorphic sensor can extract ultraviolet information from the surrounding environment, and significantly improve the recognition accuracy from 24% to 93%. This work provides a path to design neuromorphic sensors and will facilitate the potential applications in artificial vision systems.</description><subject>Neural Networks, Computer</subject><subject>Phase Transition</subject><subject>Vision, Ocular</subject><issn>2041-1723</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFjr1uwjAURi2kCqKSF2BA9wXc-i8BZlTUrR2ANTLEKEaOb-TrROrbw9DO_ZYznDN8jK2keJNCb9_JSFNvuFCKq52pal7NWKGEkVxulF6wkuguntM7uTVmzha60nVVG1Ww03eHGbmP7Xh1LUSMfMJgsw8Ozl-gYOgsOcjJRvLZY4QbJohuTNhjGjp_hTE87eQxuAzkImGiJXu52UCu_OUrWx8-jvtPPoyX3rXNkHxv00_z90P_GzwAiIxE_g</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Li, Ge</creator><creator>Xie, Donggang</creator><creator>Zhong, Hai</creator><creator>Zhang, Ziye</creator><creator>Fu, Xingke</creator><creator>Zhou, Qingli</creator><creator>Li, Qiang</creator><creator>Ni, Hao</creator><creator>Wang, Jiaou</creator><creator>Guo, Er-Jia</creator><creator>He, Meng</creator><creator>Wang, Can</creator><creator>Yang, Guozhen</creator><creator>Jin, Kuijuan</creator><creator>Ge, Chen</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><orcidid>https://orcid.org/0000-0002-4404-7957</orcidid><orcidid>https://orcid.org/0000-0001-5702-225X</orcidid><orcidid>https://orcid.org/0000-0002-8093-940X</orcidid><orcidid>https://orcid.org/0000-0001-8891-260X</orcidid><orcidid>https://orcid.org/0000-0002-0047-4375</orcidid></search><sort><creationdate>20220401</creationdate><title>Photo-induced non-volatile VO 2 phase transition for neuromorphic ultraviolet sensors</title><author>Li, Ge ; Xie, Donggang ; Zhong, Hai ; Zhang, Ziye ; Fu, Xingke ; Zhou, Qingli ; Li, Qiang ; Ni, Hao ; Wang, Jiaou ; Guo, Er-Jia ; He, Meng ; Wang, Can ; Yang, Guozhen ; Jin, Kuijuan ; Ge, Chen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-pubmed_primary_353656423</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Neural Networks, Computer</topic><topic>Phase Transition</topic><topic>Vision, Ocular</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Ge</creatorcontrib><creatorcontrib>Xie, Donggang</creatorcontrib><creatorcontrib>Zhong, Hai</creatorcontrib><creatorcontrib>Zhang, Ziye</creatorcontrib><creatorcontrib>Fu, Xingke</creatorcontrib><creatorcontrib>Zhou, Qingli</creatorcontrib><creatorcontrib>Li, Qiang</creatorcontrib><creatorcontrib>Ni, Hao</creatorcontrib><creatorcontrib>Wang, Jiaou</creatorcontrib><creatorcontrib>Guo, Er-Jia</creatorcontrib><creatorcontrib>He, Meng</creatorcontrib><creatorcontrib>Wang, Can</creatorcontrib><creatorcontrib>Yang, Guozhen</creatorcontrib><creatorcontrib>Jin, Kuijuan</creatorcontrib><creatorcontrib>Ge, Chen</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><jtitle>Nature communications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Ge</au><au>Xie, Donggang</au><au>Zhong, Hai</au><au>Zhang, Ziye</au><au>Fu, Xingke</au><au>Zhou, Qingli</au><au>Li, Qiang</au><au>Ni, Hao</au><au>Wang, Jiaou</au><au>Guo, Er-Jia</au><au>He, Meng</au><au>Wang, Can</au><au>Yang, Guozhen</au><au>Jin, Kuijuan</au><au>Ge, Chen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Photo-induced non-volatile VO 2 phase transition for neuromorphic ultraviolet sensors</atitle><jtitle>Nature communications</jtitle><addtitle>Nat Commun</addtitle><date>2022-04-01</date><risdate>2022</risdate><volume>13</volume><issue>1</issue><spage>1729</spage><pages>1729-</pages><eissn>2041-1723</eissn><abstract>In the quest for emerging in-sensor computing, materials that respond to optical stimuli in conjunction with non-volatile phase transition are highly desired for realizing bioinspired neuromorphic vision components. Here, we report a non-volatile multi-level control of VO
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films using ultraviolet irradiation and electrolyte gating, we demonstrate a proof-of-principle neuromorphic ultraviolet sensor with integrated sensing, memory, and processing functions at room temperature, and also prove its silicon compatible potential through the wafer-scale integration of a neuromorphic sensor array. The device displays linear weight update with optical writing because its metallic phase proportion increases almost linearly with the light dosage. Moreover, the artificial neural network consisting of this neuromorphic sensor can extract ultraviolet information from the surrounding environment, and significantly improve the recognition accuracy from 24% to 93%. This work provides a path to design neuromorphic sensors and will facilitate the potential applications in artificial vision systems.</abstract><cop>England</cop><pmid>35365642</pmid><doi>10.1038/s41467-022-29456-5</doi><orcidid>https://orcid.org/0000-0002-4404-7957</orcidid><orcidid>https://orcid.org/0000-0001-5702-225X</orcidid><orcidid>https://orcid.org/0000-0002-8093-940X</orcidid><orcidid>https://orcid.org/0000-0001-8891-260X</orcidid><orcidid>https://orcid.org/0000-0002-0047-4375</orcidid></addata></record> |
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source | Open Access: PubMed Central; Publicly Available Content (ProQuest); Springer Nature - Connect here FIRST to enable access; Springer Nature - nature.com Journals - Fully Open Access |
subjects | Neural Networks, Computer Phase Transition Vision, Ocular |
title | Photo-induced non-volatile VO 2 phase transition for neuromorphic ultraviolet sensors |
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