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First Hardware Demonstration of Fully Integrated Hafnia-based Multimodal Neuromorphic Computing Platform

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dc.contributor.authorPark, Eun Chan-
dc.contributor.authorSong, Minsuk-
dc.contributor.authorKim, Jangsaeng-
dc.contributor.authorLee, Jongwoo-
dc.contributor.authorKoo, Ryun-Han-
dc.contributor.authorIm, Jiseong-
dc.contributor.authorShin, Wonjun-
dc.contributor.authorKwon, Daewoong-
dc.date.accessioned2026-04-20T06:00:06Z-
dc.date.available2026-04-20T06:00:06Z-
dc.date.issued2026-01-
dc.identifier.issn0163-1918-
dc.identifier.issn2156-017X-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/212254-
dc.description.abstractWe demonstrate for the first time a hafnia-based multimodal neuromorphic computing (HMNC) platform via co-integration of volatile and nonvolatile FeTFTs. Material-device co-optimization of the FeTFTs enables diverse functionalities for multimodal data processing. Full hardware demonstration achieves robust performance for multimodal MNIST data (88%) and emotion/sentiment recognition (66%), offering a scalable and energy-efficient neuromorphic solution.-
dc.format.extent4-
dc.language영어-
dc.language.isoENG-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleFirst Hardware Demonstration of Fully Integrated Hafnia-based Multimodal Neuromorphic Computing Platform-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/IEDM50572.2025.11353684-
dc.identifier.scopusid2-s2.0-105033557081-
dc.identifier.wosid001701480300146-
dc.identifier.bibliographicCitation2025 IEEE International Electron Devices Meeting (IEDM), pp 1 - 4-
dc.citation.title2025 IEEE International Electron Devices Meeting (IEDM)-
dc.citation.startPage1-
dc.citation.endPage4-
dc.type.docTypeConference paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryComputer Science, Hardware & Architecture-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.subject.keywordPlusComputer hardware-
dc.subject.keywordPlusData handling-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/11353684-
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Kwon, Daewoong
COLLEGE OF ENGINEERING (SCHOOL OF ELECTRONIC ENGINEERING)
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