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Reducing Electron Dose and Sample Damage with Bayesian Machine Learning and Self-Organizing Neural Networks

Published online by Cambridge University Press:  25 July 2016

Karl Hujsak ,
Benjamin D. Myers ,
Eric Roth ,
Yue Li  and
Vinayak P. Dravid
Karl Hujsak
Affiliation:
Department of Materials Science & Engineering, Northwestern University, Evanston, Illinois 60208-3108
Benjamin D. Myers
Affiliation:
Department of Materials Science & Engineering, Northwestern University, Evanston, Illinois 60208-3108 Electron Probe and Instrumentation Center, NUANCE, Northwestern University, Evanston, Illinois 60208-3108
Eric Roth
Affiliation:
Department of Materials Science & Engineering, Northwestern University, Evanston, Illinois 60208-3108 Electron Probe and Instrumentation Center, NUANCE, Northwestern University, Evanston, Illinois 60208-3108
Yue Li
Affiliation:
Applied Physics Program, Northwestern University, Evanston, Illinois 60208-3108
Vinayak P. Dravid
Affiliation:
Department of Materials Science & Engineering, Northwestern University, Evanston, Illinois 60208-3108 Electron Probe and Instrumentation Center, NUANCE, Northwestern University, Evanston, Illinois 60208-3108 Applied Physics Program, Northwestern University, Evanston, Illinois 60208-3108

Abstract

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Abstract
Information
Microscopy and Microanalysis , Volume 22 , Supplement S3: Proceedings of Microscopy & Microanalysis 2016 , July 2016 , pp. 1434 - 1435
Copyright
© Microscopy Society of America 2016 

References

References:

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[4] Stevens, A., Yang, H., Carin, L., Arslan, I. & Browning, N.D. (2014). The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images. Microscopy 63(1), 41–51.CrossRefGoogle ScholarPubMed
[5] This work is based upon work supported by the Air Force Office of Scientific Research under Award No. FA9550-12-1-0280 and the Air Force Research Laboratory under Award No. FA8650-15-2-5518.Google Scholar