Machine Learning Analysis of Perovskite Oxides Grown by Molecular Beam Epitaxy

Abstract

Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and k-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial SrTiO3 growths, heteroepitaxial SrTiO3 grown on scandate substrates, BaSnO3 films grown on SrTiO3 substrates, and LaNiO3 films grown on LaAlO3 substrates. This analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control over growth processes and film quality.