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정후영

Jeong, Hu Young
UCRF Electron Microscopy group
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dc.citation.startPage 160547 -
dc.citation.title APPLIED SURFACE SCIENCE -
dc.citation.volume 669 -
dc.contributor.author Chong, Minsu -
dc.contributor.author Rhee, Tae Gyu -
dc.contributor.author Khim, Yeong Gwang -
dc.contributor.author Jung, Min-Hyoung -
dc.contributor.author Kim, Young -Min -
dc.contributor.author Jeong, Hu Young -
dc.contributor.author Kim, Heung-Sik -
dc.contributor.author Chang, Young Jun -
dc.contributor.author Kim, Hyuk Jin -
dc.date.accessioned 2024-07-24T13:35:10Z -
dc.date.available 2024-07-24T13:35:10Z -
dc.date.created 2024-07-23 -
dc.date.issued 2024-10 -
dc.description.abstract In situ reflection high-energy electron diffraction (RHEED) is a powerful technique for monitoring surface states and offers invaluable insights into thin film growth. However, extracting hidden features and subtle changes from its vast data remains as a challenge. This work bridges the gap by employing machine learning (ML)empowered RHEED analysis to elucidate the growth dynamics of two-dimensional (2D) transition metal dichalcogenide (TMDC) thin films grown under two distinct growth modes. Principal component analysis (PCA) and its modified processes were used to separate contributions of the graphene substrate and the MoSe 2 film in the RHEED video. The ML -empowered RHEED analysis allowed us to effectively filter out the strong substrate signal and reconstructing RHEED videos solely for the MoSe 2 films. This approach enabled detailed monitoring of film growth with unique features, and clearly distinguishing between the layer -by -layer growth mode and the island one. This work demonstrates the potentials of ML -empowered RHEED analysis for revealing complex growth dynamics of 2D TMDC materials, paving the way for advanced thin film monitoring and autonomous control in wider scope of thin film technologies. -
dc.identifier.bibliographicCitation APPLIED SURFACE SCIENCE, v.669, pp.160547 -
dc.identifier.doi 10.1016/j.apsusc.2024.160547 -
dc.identifier.issn 0169-4332 -
dc.identifier.scopusid 2-s2.0-85196666616 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/83292 -
dc.identifier.wosid 001260246700001 -
dc.language 영어 -
dc.publisher ELSEVIER -
dc.title Machine-learning-empowered identification of initial growth modes for 2D transition metal dichalcogenide thin films -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Chemistry, Physical; Materials Science, Coatings & Films; Physics, Applied; Physics, Condensed Matter -
dc.relation.journalResearchArea Chemistry; Materials Science; Physics -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Machine learning -
dc.subject.keywordAuthor Principal component analysis -
dc.subject.keywordAuthor Transition metal dichalcogenide -
dc.subject.keywordAuthor Growth mode -
dc.subject.keywordAuthor MBE -
dc.subject.keywordAuthor RHEED -
dc.subject.keywordPlus RHEED PATTERNS -
dc.subject.keywordPlus PEROVSKITES -
dc.subject.keywordPlus SURFACE -
dc.subject.keywordPlus ORIGIN -

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