RHEED

Clustering

Clustering algorithms are applied to identify and group statistically similar frames within the RHEED recording. This clustering is done without knowledge of the frame sequence (time order) providing robust statistical grouping.

Clusters are used to identify changes in the growth phase as they signal significant changes in the diffraction pattern's evolution.

Machine learning analysis of perovskite oxides grown by molecular beam epitaxy (Sydney, Et Al.)

Engineering ordered arrangements of oxygen vacancies at the surface of superconducting La2CuO4 thin films (Suyolcu, Et Al.)

Skill-Agnostic analysis of reflection high-energy electron diffraction patterns for Si(111) surface superstructures using machine learning (Asako Yoshinari, Et Al.)

Streak-to-Spot Ratio

This value quantifies whether you are closer to an island-like growth mode (low streak : spot ratio) or a layer-by-layer growth mode (high streak : spot ratio)

Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping (Liang, Et Al.)