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Emulating biological synaptic characteristics of HfOx/AlN-based 3D vertical resistive memory for neuromorphic systems

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dc.contributor.authorKim, Juri-
dc.contributor.authorLee, Subaek-
dc.contributor.authorSeo, Yeongkyo-
dc.contributor.authorKim, Sungjun-
dc.date.accessioned2024-08-08T11:31:50Z-
dc.date.available2024-08-08T11:31:50Z-
dc.date.issued2024-04-
dc.identifier.issn0021-9606-
dc.identifier.issn1089-7690-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/21814-
dc.description.abstractHere, we demonstrate double-layer 3D vertical resistive random-access memory with a hole-type structure embedding Pt/HfOx/AlN/TiN memory cells, conduct analog resistive switching, and examine the potential of memristors for use in neuromorphic systems. The electrical characteristics, including resistive switching, retention, and endurance, of each layer are also obtained. Additionally, we investigate various synaptic characteristics, such as spike-timing dependent plasticity, spike-amplitude dependent plasticity, spike-rate dependent plasticity, spike-duration dependent plasticity, and spike-number dependent plasticity. This synapse emulation holds great potential for neuromorphic computing applications. Furthermore, potentiation and depression are manifested through identical pulses based on DC resistive switching. The pattern recognition rates within the neural network are evaluated, and based on the conductance changing linearly with incremental pulses, we achieve a pattern recognition accuracy of over 95%. Finally, the device's stability and synapse characteristics exhibit excellent potential for use in neuromorphic systems.-
dc.format.extent10-
dc.language영어-
dc.language.isoENG-
dc.publisherAIP Publishing-
dc.titleEmulating biological synaptic characteristics of HfOx/AlN-based 3D vertical resistive memory for neuromorphic systems-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1063/5.0202610-
dc.identifier.scopusid2-s2.0-85189928957-
dc.identifier.wosid001198367000004-
dc.identifier.bibliographicCitationThe Journal of Chemical Physics, v.160, no.14, pp 1 - 10-
dc.citation.titleThe Journal of Chemical Physics-
dc.citation.volume160-
dc.citation.number14-
dc.citation.startPage1-
dc.citation.endPage10-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryChemistry, Physical-
dc.relation.journalWebOfScienceCategoryPhysics, Atomic, Molecular & Chemical-
dc.subject.keywordPlusOXIDE-BASED RRAM-
dc.subject.keywordPlusSWITCHING MEMORY-
dc.subject.keywordPlusDEVICES-
dc.subject.keywordPlusMECHANISM-
dc.subject.keywordPlusLAYER-
dc.subject.keywordAuthorNeurons-
dc.subject.keywordAuthorPattern Recognition-
dc.subject.keywordAuthorDouble Layers-
dc.subject.keywordAuthorElectrical Characteristic-
dc.subject.keywordAuthorEmbeddings-
dc.subject.keywordAuthorMemory Cell-
dc.subject.keywordAuthorMemristor-
dc.subject.keywordAuthorNeuromorphic Systems-
dc.subject.keywordAuthorRandom Access Memory-
dc.subject.keywordAuthorResistive Memory-
dc.subject.keywordAuthorResistive Switching-
dc.subject.keywordAuthorType Structures-
dc.subject.keywordAuthorRandom Access Storage-
dc.subject.keywordAuthorArtificial Neural Network-
dc.subject.keywordAuthorElectricity-
dc.subject.keywordAuthorElectricity-
dc.subject.keywordAuthorNeural Networks, Computer-
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