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Phase-Controlled Artificial SiZnSnO/P(VDF-TrFE) Synaptic Devices with a High Dynamic Range for Neuromorphic Computing

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dc.contributor.authorLee, Byeong Hyeon-
dc.contributor.authorLee, Ji Ye-
dc.contributor.authorKumar, Akash-
dc.contributor.authorLee, Sang Yeol-
dc.date.accessioned2022-12-26T02:40:10Z-
dc.date.available2022-12-26T02:40:10Z-
dc.date.created2022-10-28-
dc.date.issued2022-12-
dc.identifier.issn2199-160X-
dc.identifier.urihttps://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86316-
dc.description.abstractArtificial synapses, such as ferroelectric field-effect transistors, aspire the brain-like computation in real life and are likely to replace conventional computing methods in the future. Amorphous SiZnSnO (a-SZTO)-based ferroelectric field-effect transistor is fabricated using the organic poly(vinylidene fluoride-trifluoroethylene) P(VDF-TrFE) ferroelectric gate insulating layer. First, the ferroelectric properties of P(VDF-TrFE) are analyzed depending on the crystallization temperature for artificial synaptic transistor applications. The ferroelectricity becomes prominent with the evolution of the beta-phase till 140 degrees C and degrades thereafter. The a-SZTO-based ferroelectric field-effect transistors employing P(VDF-TrFE) show anticlockwise hysteresis, typical for a ferroelectric field-effect transistor. The nonlinearity for the potentiation and depression and the dynamic range is confirmed to be increased with higher beta-phase concentration. The rise in the concentration is related to the elevated thermodynamic stability of the beta-phase between curie temperature and the melting point. Utilizing the parameters obtained from the a-SZTO-P(VDF-TrFE) synaptic transistor, the simulation studies exhibit a high recognition rate of 86.8%, which makes it a promising candidate for artificial intelligence applications.-
dc.language영어-
dc.language.isoen-
dc.publisherWILEY-
dc.relation.isPartOfADVANCED ELECTRONIC MATERIALS-
dc.titlePhase-Controlled Artificial SiZnSnO/P(VDF-TrFE) Synaptic Devices with a High Dynamic Range for Neuromorphic Computing-
dc.typeArticle-
dc.type.rimsART-
dc.description.journalClass1-
dc.identifier.wosid000860173500001-
dc.identifier.doi10.1002/aelm.202200810-
dc.identifier.bibliographicCitationADVANCED ELECTRONIC MATERIALS, v.8, no.12-
dc.description.isOpenAccessN-
dc.identifier.scopusid2-s2.0-85138622378-
dc.citation.titleADVANCED ELECTRONIC MATERIALS-
dc.citation.volume8-
dc.citation.number12-
dc.contributor.affiliatedAuthorLee, Byeong Hyeon-
dc.contributor.affiliatedAuthorLee, Ji Ye-
dc.contributor.affiliatedAuthorKumar, Akash-
dc.contributor.affiliatedAuthorLee, Sang Yeol-
dc.type.docTypeArticle-
dc.subject.keywordAuthoramorphous oxide semiconductors-
dc.subject.keywordAuthorartificial synaptic devices-
dc.subject.keywordAuthordynamic range-
dc.subject.keywordAuthorferroelectric field-effect transistors-
dc.subject.keywordAuthorModified National Institute of Standards and Technology (MNIST)-
dc.subject.keywordAuthorP(VDF-TrFE)-
dc.subject.keywordPlusENERGY BANDGAP-
dc.subject.keywordPlusTRANSISTORS-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryNanoscience & Nanotechnology-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
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