通过深度学习进行RHEED图像序列分析以监测MBE衬底脱氧
Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning
AI总结：
针对MBE中RHEED图像依赖人工判读的问题，提出结合非监督AE与有监督卷积分类器的深度学习序列分类方法，可自动、稳健地识别GaAs衬底脱氧时刻。
摘要：
反射高能电子衍射（RHEED）是分子束外延（MBE）中的一种强大工具，但RHEED图像通常难以解读，需要经验丰富的操作人员。我们提出了一种利用基于深度学习的RHEED图像序列分类，对MBE反应室中的GaAs衬底脱氧进行自动监测的方法。该方法由用于特征提取的非监督自编码器（AE）与有监督卷积分类器网络组合而成。我们证明，轻量级网络模型能够准确识别确切的脱氧时刻。此外，我们还表明该方法非常稳健，可在数月内实现准确的脱氧检测，而无需重新训练。该方法的主要优势在于，它可以应用于原始RHEED图像，而无需旋转角、温度等额外信息。
英文摘要：
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.
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