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

  • Shuai Liu (a1), Charles N. Melton (a2), Singanallur Venkatakrishnan (a3), Ronald J. Pandolfi (a2), Guillaume Freychet (a2), Dinesh Kumar (a2), Haoran Tang (a1), Alexander Hexemer (a2) and Daniela M. Ushizima (a1) (a2)...


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.


Corresponding author

Address all correspondence to Daniela M. Ushizima at


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

  • Shuai Liu (a1), Charles N. Melton (a2), Singanallur Venkatakrishnan (a3), Ronald J. Pandolfi (a2), Guillaume Freychet (a2), Dinesh Kumar (a2), Haoran Tang (a1), Alexander Hexemer (a2) and Daniela M. Ushizima (a1) (a2)...


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