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Bipolar and Complementary Resistive Switching Characteristics and Neuromorphic System Simulation in a Pt/ZnO/TiN Synaptic Device

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dc.contributor.authorKhan, Sobia Ali-
dc.contributor.authorLee, Geun Ho-
dc.contributor.authorMahata, Chandreswar-
dc.contributor.authorIsmail, Muhammad-
dc.contributor.authorKim, Hyungjin-
dc.contributor.authorKim, Sungjun-
dc.date.accessioned2024-08-08T05:31:28Z-
dc.date.available2024-08-08T05:31:28Z-
dc.date.issued2021-02-
dc.identifier.issn2079-4991-
dc.identifier.issn2079-4991-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/18708-
dc.description.abstractIn this work, a ZnO-based resistive switching memory device is characterized by using simplified electrical conduction models. The conventional bipolar resistive switching and complementary resistive switching modes are accomplished by tuning the bias voltage condition. The material and chemical information of the device stack including the interfacial layer of TiON is well confirmed by transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) analysis. The device exhibits uniform gradual bipolar resistive switching (BRS) with good endurance and self-compliance characteristics. Moreover, complementary resistive switching (CRS) is achieved by applying the compliance current at negative bias and increasing the voltage at positive bias. The synaptic behaviors such as long-term potentiation and long-term depression are emulated by applying consecutive pulse input to the device. The CRS mode has a higher array size in the cross-point array structure than the BRS mode due to more nonlinear I-V characteristics in the CRS mode. However, we reveal that the BRS mode shows a better pattern recognition rate than the CRS mode due to more uniform conductance update.-
dc.format.extent9-
dc.language영어-
dc.language.isoENG-
dc.publisherMDPI-
dc.titleBipolar and Complementary Resistive Switching Characteristics and Neuromorphic System Simulation in a Pt/ZnO/TiN Synaptic Device-
dc.typeArticle-
dc.publisher.location스위스-
dc.identifier.doi10.3390/nano11020315-
dc.identifier.scopusid2-s2.0-85099922824-
dc.identifier.wosid000622925200001-
dc.identifier.bibliographicCitationNANOMATERIALS, v.11, no.2, pp 1 - 9-
dc.citation.titleNANOMATERIALS-
dc.citation.volume11-
dc.citation.number2-
dc.citation.startPage1-
dc.citation.endPage9-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryChemistry, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryNanoscience & Nanotechnology-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.subject.keywordAuthorresistive switching-
dc.subject.keywordAuthormemristor-
dc.subject.keywordAuthorsynaptic device-
dc.subject.keywordAuthorneural network simulation-
dc.subject.keywordAuthorX-ray photoelectron spectroscopy-
dc.subject.keywordAuthorneuromorphic system-
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