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Development in lattice strain mapping using four-dimensional scanningtransmission electron microscopy (4D-STEM) method now offers improved precisionand feasibility. However, automatic and accurate diffraction analysis is stillchallenging due to noise and the complexity of intensity in diffractionpatterns. In this work, we demonstrate an approach, employing the blobdetection on cross-correlated diffraction patterns followed by lattice fittingalgorithm, to automate the processing of four-dimensional data, includingidentifying and locating disks, and extracting local lattice parameters withoutprior knowledge about the material. The approach is both tested using simulateddiffraction patterns and applied on experimental data acquired from a Pd@Ptcore-shell nanoparticle. Our method shows robustness against various samplethicknesses and high noise, capability to handle complex patterns, andpicometer-scale accuracy in strain measurement, making it a promising tool forhigh-throughput 4D-STEM data processing.

AutoDisk: Automated Diffraction Processing and Strain Mapping in 4D-STEM