Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware

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dc.contributor.authorHan, Joon-Kyuko
dc.contributor.authorOh, Jungyeopko
dc.contributor.authorYun, Gyeong-Junko
dc.contributor.authorYoo, Dongeunko
dc.contributor.authorKim, Myung-Suko
dc.contributor.authorYu, Ji-Manko
dc.contributor.authorChoi, Sung-Yoolko
dc.contributor.authorChoi, Yang-Kyuko
dc.date.accessioned2021-08-05T07:50:22Z-
dc.date.available2021-08-05T07:50:22Z-
dc.date.created2021-06-21-
dc.date.created2021-06-21-
dc.date.issued2021-08-
dc.identifier.citationSCIENCE ADVANCES, v.7, no.32-
dc.identifier.issn2375-2548-
dc.identifier.urihttp://hdl.handle.net/10203/287043-
dc.description.abstractCointegration of multistate single-transistor neurons and synapses was demonstrated for highly scalable neuromorphic hardware, using nanoscale complementary metal-oxide semiconductor (CMOS) fabrication. The neurons and synapses were integrated on the same plane with the same process because they have the same structure of a metal-oxide semiconductor field-effect transistor with different functions such as homotype. By virtue of 100% CMOS compatibility, it was also realized to cointegrate the neurons and synapses with additional CMOS circuits. Such cointegration can enhance packing density, reduce chip cost, and simplify fabrication procedures. The multistate single-transistor neuron that can control neuronal inhibition and the firing threshold voltage was achieved for an energy-efficient and reliable neural network. Spatiotemporal neuronal functionalities are demonstrated with fabricated single-transistor neurons and synapses. Image processing for letter pattern recognition and face image recognition is performed using experimental-based neuromorphic simulation.-
dc.languageEnglish-
dc.publisherAMER ASSOC ADVANCEMENT SCIENCE-
dc.titleCointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware-
dc.typeArticle-
dc.identifier.wosid000682357400005-
dc.identifier.scopusid2-s2.0-85112003090-
dc.type.rimsART-
dc.citation.volume7-
dc.citation.issue32-
dc.citation.publicationnameSCIENCE ADVANCES-
dc.identifier.doi10.1126/sciadv.abg8836-
dc.contributor.localauthorChoi, Sung-Yool-
dc.contributor.localauthorChoi, Yang-Kyu-
dc.contributor.nonIdAuthorYun, Gyeong-Jun-
dc.contributor.nonIdAuthorYoo, Dongeun-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordPlusGAIN MODULATION-
dc.subject.keywordPlusNEURAL-NETWORK-
dc.subject.keywordPlusSPIKING-
dc.subject.keywordPlusSYSTEM-
dc.subject.keywordPlusCIRCUIT-
dc.subject.keywordPlusVISION-
dc.subject.keywordPlusMEMORY-
dc.subject.keywordPlusLATCH-
dc.subject.keywordPlusARRAY-
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