Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization. (August 2021)

• Record Type: Journal Article
• Authors: Allen, Frances I.; Pekin, Thomas C.; Persaud, Arun; Rozeveld, Steven J.; Meyers, Gregory F.; Ciston, Jim; Ophus, Colin; Minor, Andrew M.
• Is Part Of: Microscopy and microanalysis. Volume 27:Number 4(2021)
• Page Start: 794
• Page End: 803
• Publication Date: 2021-08
• Subjects: 4D-STEM -- grain orientation mapping -- NNMF -- PCA -- scanning nanobeam electron diffraction; Microscopy -- Periodicals; Microchemistry -- Periodicals; 502.82
• DOI: 10.1017/S1431927621011946
• Languages: English
• ISSNs: 1431-9276

Abstract

Abstract: 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 used 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.