Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

Frances I Allen; Thomas C Pekin; Arun Persaud; Steven J Rozeveld; Gregory F Meyers; Jim Ciston; Colin Ophus; Andrew M Minor

doi:10.1017/s1431927621011946

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arXiv · 2103.07076

Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

Frances I Allen·Thomas C Pekin·Arun Persaud·Steven J Rozeveld·Gregory F Meyers·Jim Ciston·Colin Ophus·Andrew M Minor

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Abstract

High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct electron detection. An electron probe size down to 0.5 nm in diameter is implemented and the sample investigated is a gold-palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: Principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

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Frances I Allen, Thomas C Pekin, Arun Persaud, Steven J Rozeveld, Gregory F Meyers, Jim Ciston, Colin Ophus, Andrew M Minor. 2021-03-12. Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization. https://doi.org/10.1017/s1431927621011946

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