  Reflection high-energy electron diffraction (RHEED) is a powerful tool in
molecular beam epitaxy (MBE), but RHEED images are often difficult to
interpret, requiring experienced operators. We present an approach for
automated surveillance of GaAs substrate deoxidation in MBE reactors using deep
learning based RHEED image-sequence classification. Our approach consists of an
non-supervised auto-encoder (AE) for feature extraction, combined with a
supervised convolutional classifier network. We demonstrate that our
lightweight network model can accurately identify the exact deoxidation moment.
Furthermore we show that the approach is very robust and allows accurate
deoxidation detection during months without requiring re-training. The main
advantage of the approach is that it can be applied to raw RHEED images without
requiring further information such as the rotation angle, temperature, etc.
