Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning

Abstract

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.

8 pages, 5 figures

References in corpus (4)

• scikit-image: Image processing in Python
• Recent Advances and Applications of Deep Learning Methods in Materials Science
• Empirical comparison between autoencoders and traditional dimensionality reduction methods
• Deep Learning Enabled Real Time Speckle Recognition and Hyperspectral Imaging using a Multimode Fiber Array

Cited by in corpus (4)

• A newcomer's guide to deep learning for inverse design in nano-photonics
• Deep learning for nano-photonic materials -- The solution to everything!?
• Deep learning with reflection high-energy electron diffraction images to predict cation ratio in Sr${}_{2x}$Ti${}_{2(1-x)}$O${}_3$ thin films
• Improvement of Data Analytics Techniques in Reflection High Energy Electron Diffraction to Enable Machine Learning