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Ferroelectric memristors optimized in thickness for short-term memory-driven reservoir computing

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dc.contributor.authorPark, Junhyeok-
dc.contributor.authorKim, Sungjun-
dc.date.accessioned2026-02-19T06:00:15Z-
dc.date.available2026-02-19T06:00:15Z-
dc.date.issued2026-01-
dc.identifier.issn2050-7526-
dc.identifier.issn2050-7534-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/63727-
dc.description.abstractIn this work, Mo/Hf0.5Zr0.5O2 (HZO)/n+ Si ferroelectric memristors were fabricated, and their thickness-dependent properties were systematically analyzed. The optimized 5 nm HZO device exhibited stabilized orthorhombic phase formation, yielding the highest remanent polarization (2Pr = 24.96 mu C cm-2), a tunneling electro-resistance (TER) ratio of 2474.05%, and a rectifying ratio of 4530.78. These superior properties enabled reliable multilevel cell (MLC) operation, suppressed leakage currents, and minimized sneak paths in crossbar arrays. Short-term memory (STM) characteristics were experimentally confirmed through paired-pulse facilitation (PPF) and retention measurements, validating the device's ability to emulate synaptic dynamics. Furthermore, synaptic plasticity was successfully reproduced under spike-amplitude dependent plasticity (SADP), spike-duration dependent plasticity (SDDP), spike-rate dependent plasticity (SRDP), and spike-number dependent plasticity (SNDP) conditions. Leveraging these STM features, reservoir computing (RC) simulations achieved high recognition accuracy for both MNIST and Hand MNIST datasets, with the latter reaching 98.71%, thereby demonstrating efficient processing of complex gesture data. These findings highlight the potential of the 5 nm HZO ferroelectric memristor as an optimized device for short-term memory based RC systems, offering strong potential for neuromorphic computing and next-generation memory technologies and computing.-
dc.language영어-
dc.language.isoENG-
dc.publisherRoyal Society of Chemistry-
dc.titleFerroelectric memristors optimized in thickness for short-term memory-driven reservoir computing-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1039/d5tc03983e-
dc.identifier.scopusid2-s2.0-105030532666-
dc.identifier.wosid001681432200001-
dc.identifier.bibliographicCitationJournal of Materials Chemistry C-
dc.citation.titleJournal of Materials Chemistry C-
dc.type.docTypeArticle; Early Access-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
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