Neural RHEED alignment with limited training data during CdTe MBE growth

Abstract

We introduce a data-efficient neural-vision assisted method to automate crystallographic alignment during molecular beam epitaxy (MBE) growth. Trained on reflection high-energy electron diffraction (RHEED) patterns from only 15 CdTe structures, our model - enabled by physics-aware postprocessing - reliably infers crystallographic directions, replacing manual frame-by-frame inspection. To this end, we design, test, and critically compare neural-network architectures based on 2D and 3D ResNet configurations, both with and without postprocessing that leverages the physical constraints of RHEED image acquisition. Our work delivers (i) a fully trained neural system ready for closed-loop deployment in future CdTe growth experiments and (ii) a generalizable pipeline for new materials where access to diverse RHEED datasets is limited. More broadly, this study represents a step toward AI-driven MBE growth and demonstrates the potential of machine-learning-assisted automation in thin-film synthesis.