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Effect of oxygen flow rate on long-term and short-term Schottky barrier modulations in Pd/IGZO/SiO2/p+-Si memristors

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dc.contributor.authorLee, Hee Jun-
dc.contributor.authorKim, Donguk-
dc.contributor.authorChoi, Woo Sik-
dc.contributor.authorKim, Changwook-
dc.contributor.authorChoi, Sung-Jin-
dc.contributor.authorBae, Jong-Ho-
dc.contributor.authorKim, Dong Myong-
dc.contributor.authorKim, Sungjun-
dc.contributor.authorKim, Dae Hwan-
dc.date.accessioned2024-08-08T10:02:00Z-
dc.date.available2024-08-08T10:02:00Z-
dc.date.issued2023-01-
dc.identifier.issn1369-8001-
dc.identifier.issn1873-4081-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/21342-
dc.description.abstractIn this work, we investigate the long-and short-term Schottky barrier modulations in a Pd/IGZO/SiO2/p+-Si memristor under oxygen flow rate (OFR) control. The thickness of the SiO2 layer verified using transmission electron microscopy (TEM) was found to affect resistive-switching characteristics such as the on/off ratio. A high barrier was observed corresponding to a high OFR in the thermionic emission model. In addition, we present an energy band diagram considering traps and oxygen vacancies for oxygen-rich (O-rich) and oxygen-poor (O-poor) devices. Moreover, physical parameters such as a change in barrier height, activation energy, and potentiation/ depression were experimentally extracted through various pulse schemes. Experimental results reveal that an O-rich device has high accuracy with a neural network on the MNIST dataset. In this study, we optimized the resistive-switching characteristics by varying OFR as an experimental factor. Further, we provide guidelines for designing hardware-based neuromorphic systems by separating short-and long-term components.-
dc.format.extent7-
dc.language영어-
dc.language.isoENG-
dc.publisherElsevier Ltd.-
dc.titleEffect of oxygen flow rate on long-term and short-term Schottky barrier modulations in Pd/IGZO/SiO2/p+-Si memristors-
dc.typeArticle-
dc.publisher.location네델란드-
dc.identifier.doi10.1016/j.mssp.2022.107183-
dc.identifier.scopusid2-s2.0-85140457039-
dc.identifier.wosid000880765600006-
dc.identifier.bibliographicCitationMaterials Science in Semiconductor Processing, v.153, pp 1 - 7-
dc.citation.titleMaterials Science in Semiconductor Processing-
dc.citation.volume153-
dc.citation.startPage1-
dc.citation.endPage7-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.relation.journalWebOfScienceCategoryPhysics, Condensed Matter-
dc.subject.keywordPlusDEVICE-
dc.subject.keywordAuthorNeuromorphic system-
dc.subject.keywordAuthorSynaptic device-
dc.subject.keywordAuthorMemristor-
dc.subject.keywordAuthorIndium gallium zinc oxide-
dc.subject.keywordAuthorNeuromorphic simulation-
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