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Convolutional neural networks for grazing incidence x-ray scattering patterns: thin film structure identification

Published online by Cambridge University Press:  15 March 2019

Shuai Liu ,
Charles N. Melton ,
Singanallur Venkatakrishnan ,
Ronald J. Pandolfi ,
Guillaume Freychet ,
Dinesh Kumar ,
Haoran Tang ,
Alexander Hexemer  and
Daniela M. Ushizima Open the ORCID record for Daniela M. Ushizima [Opens in a new window]
Shuai Liu
Affiliation:
University of California, Berkeley, CA 94720, USA
Charles N. Melton
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
Singanallur Venkatakrishnan
Affiliation:
Oak Ridge National Laboratory, Oakridge, TN 37830, USA
Ronald J. Pandolfi
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
Guillaume Freychet
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
Dinesh Kumar
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
Haoran Tang
Affiliation:
University of California, Berkeley, CA 94720, USA
Alexander Hexemer
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
Daniela M. Ushizima*
Affiliation:
University of California, Berkeley, CA 94720, USA Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
*
Address all correspondence to Daniela M. Ushizima at dushizima@lbl.gov
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Abstract

Nano-structured thin films have a variety of applications from waveguides, gaseous sensors to piezoelectric devices. Grazing Incidence Small Angle x-ray Scattering images enable classification of such materials. One challenge is to determine structure information from scattering patterns alone. This paper highlights the design of multiple Convolutional Neural Networks (CNN) to classify nanoparticle orientation in a thin film by learning scattering patterns. The network was trained on several thin films with a success rate of 94%. We demonstrate CNN robustness under different noises as well as demonstrate the potential of our proposed approach as a strategy to decrease scattering pattern analysis time.

Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 586 - 592
Copyright
Copyright © Materials Research Society 2019 

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