Identification and Correction of Temporal and Spatial Distortions in Scanning Transmission Electron Microscopy

TL;DR

This paper presents a new Gaussian process regression method to identify and correct spatial and temporal distortions in STEM images, improving the accuracy of atomic-scale measurements.

Contribution

It introduces a PCA-based analysis of distortions and develops a Gaussian process regression approach for quantifying and correcting STEM image distortions.

Findings

Gaussian process regression effectively quantifies distortions

Analysis workflow is provided in an accessible Jupyter notebook

Method enhances accuracy of atomic structure characterization in STEM

Abstract

Scanning transmission electron microscopy (STEM) has become the technique of choice for quantitative characterization of atomic structure of materials, where the minute displacements of atomic columns from high-symmetry positions can be used to map strain, polarization, octahedra tilts, and other physical and chemical order parameter fields. The latter can be used as inputs into mesoscopic and atomistic models, providing insight into the correlative relationships and generative physics of materials on the atomic level. However, these quantitative applications of STEM necessitate understanding the microscope induced image distortions and developing the pathways to compensate them both as part of a rapid calibration procedure for in situ imaging, and the post-experimental data analysis stage. Here, we explore the spatiotemporal structure of the microscopic distortions in STEM using multivariate analysis of the atomic trajectories in the image stacks. Based on the behavior of principal component analysis (PCA), we develop the Gaussian process (GP)-based regression method for quantification of the distortion function. The limitations of such an approach and possible strategies for implementation as a part of in-line data acquisition in STEM are discussed. The analysis workflow is summarized in a Jupyter notebook that can be used to retrace the analysis and analyze the reader's data.