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

ABSTRACT: 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 t

SUBMITTER: Kim HJ 

PROVIDER: S-EPMC9941396 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature