Free, flexible and fast: orientation mapping using the multi-core and GPU-accelerated template matching capabilities in the python-based open source 4D-STEM analysis toolbox Pyxem

Cautaerts, Niels; Crout, Phillip; Ånes, Håkon Wiik; Prestat, Éric; Jeong, Jiwon; Dehm, Gerhard; Liebscher, Christian

doi:10.48550/arxiv.2111.07347

Cited by 1 publication

(1 citation statement)

References 26 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, if the dataset being analyzed contains a large number of different sample thicknesses, multiple scattering can cause strong oscillations in the peak amplitude values. The intensity weighting factor v provides a similar effect as the "gamma correction" used in many diffraction template matching routines (Cautaerts et al, 2021), but acts on the measured disk intensities rather than the original diffraction pattern. As we will see in the simulations below, in these situations the best results may be achieved by setting v = 0, that is ignoring peak intensity and weighting only by the peak radii.…”

Section: Correlation Pattern Matchingmentioning

confidence: 99%

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

et al. 2022

View full text Add to dashboard Cite

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 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 demonstrate 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.

show abstract

Section: Correlation Pattern Matchingmentioning

confidence: 99%