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

doi:10.1186/s40580-023-00359-5

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

Journal Article · Mon Feb 20 00:00:00 EST 2023 · Nano Convergence

DOI:https://doi.org/10.1186/s40580-023-00359-5· OSTI ID:2470879

Kim, Hyuk Jin ^[1]; Chong, Minsu ^[1]; Rhee, Tae Gyu ^[1]; Khim, Yeong Gwang ^[1]; Jung, Min-Hyoung ^[2]; Kim, Young-Min ^[2]; Jeong, Hu Young ^[3]; Choi, Byoung Ki ^[4]; ^[1]

University of Seoul (Korea, Republic of)
Sungkyunkwan University (SKKU), Suwon (Korea, Republic of)
Ulsan National Institute of Science and Technology (UNIST) (Korea, Republic of)
University of Seoul (Korea, Republic of); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2470879

Journal Information:: Nano Convergence, Journal Name: Nano Convergence Journal Issue: 1 Vol. 10; ISSN 2196-5404

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
K-means clustering
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Principal component analysis
RHEED
ReSe2
Science & Technology - Other Topics
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Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

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