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Sunday Short Course at M&M2021 (Virtual)

LEAD INSTRUCTORS:
David Muller, Cornell University
Colin Ophus, Lawrence Berkeley National Laboratory

With modern electron detector technology, it is now possible to record full images of a converged STEM probe while scanning it over the sample surface, resulting in a 4D-STEM dataset. Because the atomic-scale scattering information contained in an atomic-scale STEM probe is decoupled from the step size between STEM probe positions, 4D-STEM can be used for experiments ranging from sub-Angstrom resolution phase contrast imaging to statistical characterization of functional materials over large length scales. In this course, we will give tutorials on how to perform 4D-STEM experiments, analyze the (potentially very large!) resulting datasets, and perform 4D-STEM simulations.

Sunday Short Course at M&M 2021 (Virtual)

LEAD INSTRUCTORS:
Eric Prestat, University of Manchester and SuperSTEM Laboratory, United Kingdom
Joshua Taillon, National Institute of Standards and Technology

This short course will introduce the use of HyperSpy and related Python libraries (atomap, pixStem, pyXem) for analysis of microscopy datasets. No prior Python knowledge is required. Attendees will learn how to perform basic machine learning, multi-dimensional curve fitting for EELS and EDS quantification, atomic resolution image analysis and big data processing (such as 4D STEM) on desktop computers.