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Autonomous Detection and Identification of Defects in Nanoscale Devices using Electron Diffraction Imaging

Published online by Cambridge University Press:  22 July 2022

Jian-Min Zuo ,
Renliang Yuan  and
Jiong Zhang
Jian-Min Zuo*
Affiliation:
Materials Science and Engineering, University of Illinois, Urbana, IL, USA
Renliang Yuan
Affiliation:
Materials Science and Engineering, University of Illinois, Urbana, IL, USA
Jiong Zhang
Affiliation:
Intel Corporation, Corporate Quality Network, Hillsboro, OR, USA
*
*Corresponding author: [email protected]

Abstract

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Type
Quantum Materials Under Electron Beam: From Atomic Structures to Working Devices
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 1762 - 1763
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Copyright
Copyright © Microscopy Society of America 2022

References

Whiteley, SJ et al. , Nature Communications 10 (2019), p. 3386.CrossRefGoogle Scholar
Smith, DJ, Progress in Crystal Growth and Characterization of Materials 66 (2020), p. 100498.CrossRefGoogle Scholar
Lee, C-H et al. , Nano Letters 20 (2020), p. 3369.Google Scholar
Zuo, J-M, et al. , Microscopy (2022), In print.Google ScholarPubMed
Voulodimos, A et al. , Deep Learning for Computer Vision: A Brief Review, Computational Intelligence and Neuroscience (2018), p. 7068349.CrossRefGoogle ScholarPubMed
Work supported by Intel and Grainger College of Engineering, University of Illinois.Google Scholar