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Deep Learning Enabled Measurements of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-Picometer Precision

Published online by Cambridge University Press:  05 August 2019

Chia-Hao Lee
Affiliation:
Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Chuqiao Shi
Affiliation:
Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Di Luo
Affiliation:
Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Abid Khan
Affiliation:
Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Blanka E. Janicek
Affiliation:
Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Sangmin Kang
Affiliation:
Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Wenjuan Zhu
Affiliation:
Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Bryan K. Clark
Affiliation:
Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Pinshane Y. Huang*
Affiliation:
Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
*
*Corresponding author: [email protected]

Abstract

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Type
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Ziatdinov, M, et al. , ACS Nano 11 (2017), p. 12742.Google Scholar
[2]Savitzky, B, et al. , Ultramicroscopy 191 (2018), p. 56.Google Scholar
[3]This work was supported by the AFOSR under award number FA9550-7-1-0213 and carried out in part in the Materials Research Laboratory at UIUC. S.K. and W.Z. would like to acknowledge the support from ONR under grant NAVY N00014-17-1-2973.Google Scholar