Decoding Material Structures with Scanning Electron Diffraction Techniques

Abstract

Recent advancements in electron detectors and computing power have revolutionized the rapid recording of millions of 2D diffraction patterns across a grid of probe positions, known as four-dimensional scanning transmission electron microscopy (4D-STEM). These datasets serve as the foundation for innovative STEM imaging techniques like integrated center of mass (iCOM) and symmetry STEM (S-STEM). This paper delves into the application of 4D-STEM datasets for diffraction analysis. We therefore use the term scanning electron diffraction (SED) instead of 4D-STEM in this review. We comprehensively explore groundbreaking diffraction methods based on SED, structured into two main segments: (i) utilizing an atomic-scale electron probe and (ii) employing a nanoscale electron probe. Achieving an atomic-scale electron probe necessitates a significant convergence angle (α > 30 mrad), leading to interference between direct and diffracted beams, distinguishing it from its nanoscale counterpart. Additionally, integrating machine learning approaches with SED experiments holds promise in various directions, as discussed in this review. Our aim is to equip materials scientists with valuable insights for characterizing atomic structures using cutting-edge SED techniques.