RHEED pattern classification by a convolutional neural network for the growth of chalcogenide thin films and nanostructures

The use of reflection high energy electron diffraction (RHEED) plays a critical role in identifying thin film morphology. However, analysis of RHEED patterns depends on skilled experts and thus is difficult to incorporate into an automated synthesis process in real-time. In this study, we develop a convolutional neural network model that can accurately classify four common and distinct RHEED patterns deriving from three separate growth modes encountered in chalcogenide thin film growth, at nearly 95% accuracy. Our findings show that ML techniques can be successfully implemented even in cases where there is no detailed knowledge of growth chemistry, providing an avenue towards real-time incorporation of ML to control nanostructure nucleation and thin film morphology.