Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

TL;DR

This paper introduces an automated method for crystal orientation mapping using sparse correlation matching in electron diffraction patterns, enabling fast, accurate analysis of complex polycrystalline samples.

Contribution

The authors developed a novel automated orientation mapping algorithm using sparse correlation, applicable to complex samples and validated with simulations and experimental data.

Findings

High accuracy in indexing diffraction patterns.

Effective mapping of complex polycrystalline structures.

Open source implementation available for community use.

Abstract

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We therefore have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We test the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.