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

Liu, Shuai; Melton, Charles; Venkatakrishnan, Singanallur; Pandolfi, Ronald; Freychet, Guillaume; Kumar, Dinesh; Tang, Haoran; Hexemer, Alexander; Ushizima, Daniela

doi:10.1557/mrc.2019.26

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

Journal Article · Fri Mar 15 00:00:00 EDT 2019 · MRS Communications

DOI:https://doi.org/10.1557/mrc.2019.26· OSTI ID:1566998

Liu, Shuai ^[1]; Melton, Charles ^[2]; ^[3]; Pandolfi, Ronald ^[2]; Freychet, Guillaume ^[2]; Kumar, Dinesh ^[2]; Tang, Haoran ^[2]; Hexemer, Alexander ^[2]; Ushizima, Daniela ^[2]

Univ. of California, Berkeley, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: Alfred P. Sloan Foundation; Gordon and Betty Moore Foundation (GBMF); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Scientific User Facilities Division

Grant/Contract Number:: AC02-05CH11231; AC05-00OR22725

OSTI ID:: 1566998

Alternate ID(s):: OSTI ID: 1581346

Journal Information:: MRS Communications, Journal Name: MRS Communications Journal Issue: 02 Vol. 9; ISSN 2159-6859; ISSN applab

Publisher:: Materials Research Society - Cambridge University PressCopyright Statement

Country of Publication:: United States

Language:: English

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