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Cited 12 time in webofscience Cited 12 time in scopus
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Enhanced Anion Interaction by Polarity Control on CNTVT:SVS Copolymers for Improving Nonvolatile Characteristics in Neuromorphic Computing

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dc.contributor.authorLee, Donghwa-
dc.contributor.authorAyuningtias, Landep-
dc.contributor.authorHwang, Jinwoo-
dc.contributor.authorSung, Junho-
dc.contributor.authorKang, Joonhee-
dc.contributor.authorKim, Yun-Hi-
dc.contributor.authorLee, Eunho-
dc.date.accessioned2024-05-29T00:30:23Z-
dc.date.available2024-05-29T00:30:23Z-
dc.date.issued2024-05-
dc.identifier.issn2639-4979-
dc.identifier.issn2639-4979-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/70635-
dc.description.abstractSynaptic devices that simulate biological functions are of interest in neuromorphic computing, because of their low power consumption characteristics. However, achieving long-term plasticity (LTP) in electrolyte-gated transistors (EGTs) is challenging, because the electric double layer (EDL) of the electrolyte/channel disappears when the gate electrode voltage is removed. In this study, we fabricated a CNTVT-based EGTs by adjusting the polarity of the backbone. This process involves improving the polarity of the backbone by adjusting the DPP-CNTVT ratio. Furthermore, it facilitates increased binding of TFSI anions in DEME-TFSI at the electrolyte/channel interface. The CNTVT-based EGTs successfully achieved LTP and exhibited essential synaptic properties, including paired-pulse facilitation (PPF) and a high-pass filter. Furthermore, the results of driving MNIST handwritten digits based on long-term potentiation/depression (LTP/LTD) with controlled backbone polarity improved from 50.18% to 93.28%. These findings offer a simple architectural design for synaptic devices that leverage state-of-the-art neural modeling techniques. © 2024 American Chemical Society.-
dc.format.extent10-
dc.language영어-
dc.language.isoENG-
dc.publisherAMER CHEMICAL SOC-
dc.titleEnhanced Anion Interaction by Polarity Control on CNTVT:SVS Copolymers for Improving Nonvolatile Characteristics in Neuromorphic Computing-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1021/acsmaterialslett.4c00087-
dc.identifier.scopusid2-s2.0-85193586550-
dc.identifier.wosid001232682200001-
dc.identifier.bibliographicCitationACS Materials Letters, v.6, no.5, pp 2329 - 2338-
dc.citation.titleACS Materials Letters-
dc.citation.volume6-
dc.citation.number5-
dc.citation.startPage2329-
dc.citation.endPage2338-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.subject.keywordPlusTOTAL-ENERGY CALCULATIONS-
dc.subject.keywordPlusTERM SYNAPTIC PLASTICITY-
dc.subject.keywordPlusTRANSISTORS-
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