Sunday Short Courses

Sunday, August 2, 2021 — David L. Lawrence Convention Center

X-12 Guidelines for Performing 4D-STEM Characterization from the Atomic to >Micrometer Scales: Experimental Considerations, Data Analysis and Simulation

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.

X-15 Data Analysis in Materials Science

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.

For this hands-on and interactive short course, attendees will need to install software on their own laptop in advance and bring it with them to the short course (instructions will be provided).