Improvement of Data Analytics Techniques in Reflection High Energy Electron Diffraction to Enable Machine Learning

Perovskite oxides such as LaFeO$_3$ are a well-studied family of materials that possess a wide range of useful and novel properties. Successfully synthesizing perovskite oxide samples usually requires a significant number of growth attempts and a detailed film characterization on each sample to find the optimal growth window of a material. The most common real-time \textit{in situ} diagnostic technique available during molecular beam epitaxy (MBE) synthesis is reflection high-energy electron diffraction (RHEED). Conventional use of RHEED allows a highly experienced operator to determine growth rate by monitoring intensity osciallations and make some qualitative observations during growth, such as recognizing the sample has become amorphous or recognizing that large islands have formed on the surface. However, due to a lack of theoretical understanding of the diffraction patterns, finer, more precise levels of observations are challenging. To address these limitations, we implement new data analytics techniques in the growth of three LaFeO$_3$ samples on Nb-doped SrTiO$_3$ by MBE. These techniques improve our ability to perform unsupervised machine learning using principal component analysis (PCA) and k-means clustering by using drift correction to overcome sample or stage motion during growth and intensity transformations that highlight more subtle features in the images such as Kikuchi bands. With this approach, we enable the first demonstration of PCA and k-means across multiple samples, allowing for quantitative comparison of RHEED videos for two LaFeO$_3$ film samples. These capabilities set the stage for real-time processing of RHEED data during growth to enable machine learning-accelerated film synthesis.