Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization
Allen, Frances I, Pekin, Thomas C, Persaud, Arun, Rozeveld, Steven J, Meyers, Gregory F, Ciston, Jim, Ophus, Colin, Minor, Andrew M
Abstract
High-throughput grain mapping with sub-nanometer spatial resolution is\ndemonstrated using scanning nanobeam electron diffraction (also known as 4D\nscanning transmission electron microscopy, or 4D-STEM) combined with high-speed\ndirect electron detection. An electron probe size down to 0.5 nm in diameter is\nimplemented and the sample investigated is a gold-palladium nanoparticle\ncatalyst. Computational analysis of the 4D-STEM data sets is performed using a\ndisk registration algorithm to identify the diffraction peaks followed by\nfeature learning to map the individual grains. Two unsupervised feature\nlearning techniques are compared: Principal component analysis (PCA) and\nnon-negative matrix factorization (NNMF). The characteristics of the PCA versus\nNNMF output are compared and the potential of the 4D-STEM approach for\nstatistical analysis of grain orientations at high spatial resolution is\ndiscussed.