Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth.

Kim, Hyuk Jin; Chong, Minsu; Rhee, Tae Gyu; Khim, Yeong Gwang; Jung, Min-Hyoung; Kim, Young-Min; Jeong, Hu Young; Choi, Byoung Ki; Chang, Young Jun

Kim, Hyuk Jin; Chong, Minsu; Rhee, Tae Gyu; Khim, Yeong Gwang; Jung, Min-Hyoung; Kim, Young-Min; Jeong, Hu Young; Choi, Byoung Ki; Chang, Young Jun.

Afiliación

Kim HJ; Department of Physics, University of Seoul, Seoul, 02504, Republic of Korea.
Chong M; Department of Physics, University of Seoul, Seoul, 02504, Republic of Korea.
Rhee TG; Department of Physics, University of Seoul, Seoul, 02504, Republic of Korea.
Khim YG; Department of Smart Cities, University of Seoul, Seoul, 02504, Republic of Korea.
Jung MH; Department of Physics, University of Seoul, Seoul, 02504, Republic of Korea.
Kim YM; Department of Smart Cities, University of Seoul, Seoul, 02504, Republic of Korea.
Jeong HY; Department of Energy Science, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea.
Choi BK; Department of Energy Science, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea.
Chang YJ; Graduate School of Semiconductor Materials and Devices Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea.

Nano Converg ; 10(1): 10, 2023 Feb 20.

Article en En | MEDLINE | ID: mdl-36806667

Palabras clave

K-means clustering; Machine learning; Principal component analysis; RHEED; ReSe2; TMDC

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nano Converg Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido

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