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

Kevin M. Roccapriore; Matthew G. Boebinger; Ondrej Dyck; Ayana Ghosh; Raymond R. Unocic; Sergei V. Kalinin; Maxim Ziatdinov

doi:10.1021/acsnano.2c07451

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

Kevin M. Roccapriore, Matthew G. Boebinger, Ondrej Dyck, Ayana Ghosh, Raymond R. Unocic, Sergei V. Kalinin, Maxim Ziatdinov

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

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.

Original language	English
Pages (from-to)	17116-17127
Number of pages	12
Journal	ACS Nano
Volume	16
Issue number	10
DOIs	https://doi.org/10.1021/acsnano.2c07451
State	Published - Oct 25 2022

Funding

This research (ELIT workflows, integration with Nion-Swift) is sponsored by the INTERSECT Initiative as part of the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. The STEM experiments were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, and Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.

Funders	Funder number
CNMS
Oak Ridge National Laboratory
U.S. Department of Energy	DE-AC05-00OR22725
Office of Science
Basic Energy Sciences
Oak Ridge National Laboratory

Keywords

atomic defects
atomic fabrication
automated experiment
deep learning
electron beam patterning
electron irradiation
scanning transmission electron microscopy

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10.1021/acsnano.2c07451

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

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.",

keywords = "atomic defects, atomic fabrication, automated experiment, deep learning, electron beam patterning, electron irradiation, scanning transmission electron microscopy",

author = "Roccapriore, {Kevin M.} and Boebinger, {Matthew G.} and Ondrej Dyck and Ayana Ghosh and Unocic, {Raymond R.} and Kalinin, {Sergei V.} and Maxim Ziatdinov",

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AU - Ghosh, Ayana

AU - Unocic, Raymond R.

AU - Kalinin, Sergei V.

AU - Ziatdinov, Maxim

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AB - 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.

KW - atomic defects

KW - atomic fabrication

KW - automated experiment

KW - deep learning

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KW - electron irradiation

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

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