Variational Convolutional Autoencoders for Anomaly Detection in Scanning Transmission Electron Microscopy

Identifying point defects and other structural anomalies using scanning transmission electron microscopy (STEM) is important to understand a material's properties caused by the disruption of the regular pattern of crystal lattice. Due to improvements in instrumentation stability and electron op...

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Bibliographic Details
Published in:	Small (Weinheim an der Bergstrasse, Germany) Vol. 19; no. 16; pp. e2205977 - n/a
Main Authors:	Prifti, Enea, Buban, James P., Thind, Arashdeep Singh, Klie, Robert F.
Format:	Journal Article
Language:	English
Published:	Germany Wiley Subscription Services, Inc 01.04.2023
Subjects:	Anomalies anomaly detection Columns (structural) Computer vision convolutional neural networks Crystal defects Crystal lattices electron microscopy Electron optics Electrons Field of view Image analysis Machine learning Nanotechnology Point defects Scanning electron microscopy Scanning transmission electron microscopy Transmission electron microscopy variational convolutional autoencoders electron microscopy anomaly detection variational convolutional autoencoders convolutional neural networks machine learning
ISSN:	1613-6810, 1613-6829, 1613-6829
Online Access:	Get full text
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