Monitoring MBE Substrate Deoxidation via RHEED Image-Sequence Analysis by Deep Learning

doi:10.1021/acs.cgd.2c01132

Monitoring MBE Substrate Deoxidation via RHEED Image-Sequence Analysis by Deep Learning

2023-01-16

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Abdourahman Khaireh-Walieh

DOI：10.1021/acs.cgd.2c01132

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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 nonsupervised autoencoder (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 for months without requiring retraining. 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.

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Monitoring MBE Substrate Deoxidation via RHEED Image-Sequence Analysis by Deep Learning

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