Skill-Agnostic analysis of reflection high-energy electron diffraction patterns for Si(111) surface superstructures using machine learning

Reflection high-energy electron diffraction (RHEED) data are important for the in-situ characterization of surface conditions during physical vapor deposition. Surface superstructures obtained by adsorbing exotic atoms onto a clean silicon surface, which exhibit various physical properties, were identified using RHEED. However, this information is too abundant for quantitative analysis; therefore, scientists rely on their expertise to interpret RHEED patterns to assess surface structures and evaluate film thickness, and a large amount of information remains unused. In this study, we adapted machine learning for a RHEED pattern dataset of a clean Si(111) surface during indium deposition in molecular-beam epitaxy growth to use the entire RHEED pattern image information and investigated appropriate machine leaning analysis methods. First, we aimed to determine RHEED pattern similarities in the dataset. Then, five structural phases, 7 × 7 (clean surface), √3×√3, √31×√31, 4 × 1, and 4 × 1 (Room Temperature), were automatically detected by hierarchical clustering using Ward’s method. Next, we aimed to extract the information for each surface superstructure from the dataset. Using non-negative matrix factorization, we successfully estimated the optimal forming conditions for each surface superstructure separately more accurately than the conventional methods. Our proposed strategies could be widely applied to surface structural analysis.