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Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning

Published online by Cambridge University Press:  08 September 2022

Alexandra Bruefach Open the ORCID record for Alexandra Bruefach [Opens in a new window] ,
Colin Ophus  and
Mary C. Scott
Alexandra Bruefach
Affiliation:
Department of Materials Science and Engineering, University of California, Berkeley, CA 94720, USA
Colin Ophus
Affiliation:
National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Mary C. Scott*
Affiliation:
Department of Materials Science and Engineering, University of California, Berkeley, CA 94720, USA National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
*
*Corresponding author: Mary C. Scott, E-mail: [email protected]
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Abstract

Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.

Keywords

4D-STEMdiffractionmachine learningnanomaterialsscanning transmission electron microscopy
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 28 , Issue 6 , December 2022 , pp. 1998 - 2008
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of the Microscopy Society of America

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