Machine-learning-enabled on-the-fly analysis of RHEED patterns during thin film deposition by molecular beam epitaxy

Thin film deposition is a fundamental technology for the discovery, optimization, and manufacturing of functional materials. Deposition by molecular beam epitaxy (MBE) typically employs reflection high energy electron diffraction (RHEED) as a real-time in situ probe of the growing film. However, the state of the art for RHEED analysis during deposition requires human observation. Here we present an approach using machine learning (ML) methods to monitor, analyze, and interpret RHEED images on-the-fly during thin film deposition. In the analysis workflow, RHEED pattern images are collected at one frame per second and featurized using a pretrained deep convolutional neural network. The feature vectors are then statistically analyzed to identify changepoints; these changepoints can be related to changes in deposition mode from initial film nucleation to a transition regime, smooth film deposition, and in some cases an additional transition to a rough, islanded deposition regime. The feature vectors are additionally analyzed via graph analysis and community classification. The graph is quantified as a stabilization plot, and we show that inflection points in the stabilization plot correspond to changes in growth regime. The full RHEED analysis workflow is termed RHAAPsody and includes data transfer and output to a visual dashboard. We demonstrate the functionality of RHAAPsody by analyzing pre-captured RHEED images from epitaxial depositions of anatase TiO₂ on SrTiO₃(001) and show that the analysis workflow can be executed in less than one second. Our approach shows promise as one component of ML-enabled real-time feedback control of the MBE deposition process.