Variational Convolutional Autoencoders for Anomaly Detection in Scanning Transmission Electron Microscopy

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. Automated analysis of atomic‐resolution scanning transmission electron microscopy images is demonstrated using a neural network approach. A Convolutional Variational Autoencoder (CVAE) architecture is used to detect crystal‐structure anomalies, such as point defects, in the bulk of SrTiO3 [001] with a training set that is limited to perfect bulk images. The performance of the CVAE in differentiating between bulk from point defects is quantified.