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dc.contributor.authorKim, Sangheon-
dc.contributor.authorKang, Unhyeon-
dc.contributor.authorGu, Jiyoung-
dc.contributor.authorKim, Jaewook-
dc.contributor.authorPark, Jongkil-
dc.contributor.authorHwang, Gyu Weon-
dc.contributor.authorPark, Seongsik-
dc.contributor.authorJang, Hyun Jae-
dc.contributor.authorSeong, Tae-Yeon-
dc.contributor.authorLee, Suyoun-
dc.date.accessioned2024-07-11T06:30:26Z-
dc.date.available2024-07-11T06:30:26Z-
dc.date.created2024-07-11-
dc.date.issued2024-07-
dc.identifier.issn1944-8244-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/150219-
dc.description.abstractAssociative multimodal artificial intelligence (AMAI) has gained significant attention across various fields, yet its implementation poses challenges due to the burden on computing and memory resources. To address these challenges, researchers have paid increasing attention to neuromorphic devices based on novel materials and structures, which can implement classical conditioning behaviors with simplified circuitry. Herein, we introduce an artificial multimodal neuron device that shows not only the acquisition behavior but also the extinction and the spontaneous recovery behaviors for the first time. Being composed of an ovonic threshold switch (OTS)-based neuron device, a conductive bridge memristor (CBM)-based synapse device, and a few passive electrical elements, such observed behaviors of this neuron device are explained in terms of the electroforming and the diffusion of metallic ions in the CBM. We believe that the proposed associative learning neuron device will shed light on the way of developing large-scale AMAI systems by providing inspiration to devise an associative learning network with improved energy efficiency.-
dc.languageEnglish-
dc.publisherAmerican Chemical Society-
dc.titleArtificial Multimodal Neuron with Associative Learning Capabilities: Acquisition, Extinction, and Spontaneous Recovery-
dc.typeArticle-
dc.identifier.doi10.1021/acsami.4c02343-
dc.description.journalClass1-
dc.identifier.bibliographicCitationACS Applied Materials & Interfaces, v.16, no.28, pp.36519 - 36526-
dc.citation.titleACS Applied Materials & Interfaces-
dc.citation.volume16-
dc.citation.number28-
dc.citation.startPage36519-
dc.citation.endPage36526-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.identifier.wosid001260668000001-
dc.identifier.scopusid2-s2.0-85197433059-
dc.relation.journalWebOfScienceCategoryNanoscience & Nanotechnology-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.relation.journalResearchAreaMaterials Science-
dc.type.docTypeArticle-
dc.subject.keywordAuthormultimodal-
dc.subject.keywordAuthorassociative learning-
dc.subject.keywordAuthorclassicalconditioning-
dc.subject.keywordAuthorartificial neuron-
dc.subject.keywordAuthorneuromorphic device-
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