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Updated: Aug 26, 2025

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
Published on: May 28, 2016
Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron
Kevin M Roccapriore1, Matthew G Boebinger1, Ondrej Dyck1
1Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee37831, United States.
Abstract:
A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.
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