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One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications

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dc.contributor.authorHam, Seonggil-
dc.contributor.authorKang, Minji-
dc.contributor.authorJang, Seonghoon-
dc.contributor.authorJang, Jingon-
dc.contributor.authorChoi, Sanghyeon-
dc.contributor.authorKim, Tae-Wook-
dc.contributor.authorWang, Gunuk-
dc.date.accessioned2021-08-30T19:47:46Z-
dc.date.available2021-08-30T19:47:46Z-
dc.date.created2021-06-19-
dc.date.issued2020-07-
dc.identifier.issn2375-2548-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/54472-
dc.description.abstractOne-dimensional (1D) devices are becoming the most desirable format for wearable electronic technology because they can be easily woven into electronic (e-) textile(s) with versatile functional units while maintaining their inherent features under mechanical stress. In this study, we designed 1D fiber-shaped multi-synapses comprising ferroelectric organic transistors fabricated on a 100-mu m Ag wire and used them as multisynaptic channels in an e-textile neural network for wearable neuromorphic applications. The device mimics diverse synaptic functions with excellent reliability even under 6000 repeated input stimuli and mechanical bending stress. Various NOR-type textile arrays are formed simply by cross-pointing 1D synapses with Ag wires, where each output from individual synapse can be integrated and propagated without undesired leakage. Notably, the 1D multi-synapses achieved up to similar to 90 and similar to 70% recognition accuracy for MNIST and electrocardiogram patterns, respectively, even in a single-layer neural network, and almost maintained regardless of the bending conditions.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherAMER ASSOC ADVANCEMENT SCIENCE-
dc.subjectTIMING-DEPENDENT PLASTICITY-
dc.subjectDEVICES-
dc.subjectCLASSIFICATION-
dc.subjectTRANSISTORS-
dc.subjectSENSOR-
dc.subjectARRAY-
dc.titleOne-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications-
dc.typeArticle-
dc.contributor.affiliatedAuthorWang, Gunuk-
dc.identifier.doi10.1126/sciadv.aba1178-
dc.identifier.scopusid2-s2.0-85090534524-
dc.identifier.wosid000548735600008-
dc.identifier.bibliographicCitationSCIENCE ADVANCES, v.6, no.28-
dc.relation.isPartOfSCIENCE ADVANCES-
dc.citation.titleSCIENCE ADVANCES-
dc.citation.volume6-
dc.citation.number28-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.relation.journalWebOfScienceCategoryMultidisciplinary Sciences-
dc.subject.keywordPlusTIMING-DEPENDENT PLASTICITY-
dc.subject.keywordPlusDEVICES-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusTRANSISTORS-
dc.subject.keywordPlusSENSOR-
dc.subject.keywordPlusARRAY-
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