Department of Physics, University of Illinois Chicago, 845 W Taylor Street, Chicago, IL, 60607, USA.
Small. 2023 Apr;19(16):e2205977. doi: 10.1002/smll.202205977. Epub 2023 Jan 18.
Identifying point defects and other structural anomalies using scanning transmission electron microscopy (STEM) is important to understand a material's properties caused by the disruption of the regular pattern of crystal lattice. Due to improvements in instrumentation stability and electron optics, atomic-resolution images with a field of view of several hundred nanometers can now be routinely acquired at 1-10 Hz frame rates and such data, which often contain thousands of atomic columns, need to be analyzed. To date, image analysis is performed largely manually, but recent developments in computer vision (CV) and machine learning (ML) now enable automated analysis of atomic structures and associated defects. Here, the authors report on how a Convolutional Variational Autoencoder (CVAE) can be utilized to detect structural anomalies in atomic-resolution STEM images. Specifically, the training set is limited to perfect crystal images , and the performance of a CVAE in differentiating between single-crystal bulk data or point defects is demonstrated. It is found that the CVAE can reproduce the perfect crystal data but not the defect input data. The disagreesments between the CVAE-predicted data for defects allows for a clear and automatic distinction and differentiation of several point defect types.
使用扫描透射电子显微镜（STEM）识别点缺陷和其他结构异常对于理解由于晶格规则图案的破坏而导致的材料性质非常重要。由于仪器稳定性和电子光学的改进，现在可以以 1-10 Hz 的帧率常规地获得具有几百纳米视场的原子分辨率图像，并且通常需要分析包含数千个原子列的数据。迄今为止，图像分析主要是手动进行的，但计算机视觉（CV）和机器学习（ML）的最新进展现在使原子结构和相关缺陷的自动分析成为可能。在这里，作者报告了卷积变分自动编码器（CVAE）如何用于检测原子分辨率 STEM 图像中的结构异常。具体来说，训练集仅限于完美晶体图像，并且演示了 CVAE 在区分单晶体数据或点缺陷方面的性能。结果发现，CVAE 可以再现完美晶体数据，但不能再现缺陷输入数据。CVAE 预测缺陷的数据之间的差异允许对几种点缺陷类型进行清晰和自动的区分和区分。
