Exploring deep learning models for 4D-STEM-DPC data processing

Abstract

For the study of magnetic materials at the nanoscale, differential phase contrast (DPC) imaging is a potent tool. With the advancements in direct detector technology, and consequent popularity gain for four-dimensional scanning transmission electron microscopy (4D-STEM), there has been an ongoing development of new and enhanced ways for STEM-DPC big data processing. Conventional algorithms are experimentally tailored, and so in this article we explore how supervised learning with convolutional neural networks (CNN) can be utilized for automated and consistent processing of STEM-DPC data. Two different approaches are investigated, one with direct tracking of the beam with regression analysis, and one where a modified U-net is used for direct beam segmentation as a pre-processing step. The CNNs are trained on experimentally obtained 4D-STEM data, enabling them to effectively handle data collected under similar instrument acquisition parameters. The model outputs are compared to conventional algorithms, particularly in how they process data in the presence of strong diffraction contrast, and how they affect domain wall profiles and width measurement.