Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscopy

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 spec...

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Bibliographic Details
Published in:ACS nano 2022-10, Vol.16 (10), p.17116-17127
Main Authors: Roccapriore, Kevin M., Boebinger, Matthew G., Dyck, Ondrej, Ghosh, Ayana, Unocic, Raymond R., Kalinin, Sergei V., Ziatdinov, Maxim
Format: Article
Language:English
Subjects:
atomic defects
atomic fabrication
automated experiment
deep learning
defects
electron beam patterning
electron irradiation
imaging
INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
manufacturing
scanning transmission electron microscopy
two dimensional materials
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Summary: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.
ISSN:1936-0851
1936-086X
DOI:10.1021/acsnano.2c07451