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.
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Acknowledgments
The authors acknowledge authors Ye, Zhu, Ercius, Raja, He, Jones, Hauwiller, Liu, Xu, and Alivisatos for allowing us to use their data for our real experiment analysis. This work was supported by the Center of Advanced Mathematics for Energy Research Applications (CAMERA) through the Office of Science, of the US Department of Energy under Contract No. DE-AC02-05CH11231 and the Early Career Program. This research is funded in part by the Gordon and Betty Moore Foundation through Grant GBMF3834 and by the Alfred P. Sloan Foundation through Grant 2013-10-27 to the University of California, Berkeley. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a US Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.
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The supplementary material for this article can be found at https://doi.org/10.1557/mrc.2019.26
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Liu, S., Melton, C.N., Venkatakrishnan, S. et al. Convolutional neural networks for grazing incidence x-ray scattering patterns: thin film structure identification. MRS Communications 9, 586–592 (2019). https://doi.org/10.1557/mrc.2019.26
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DOI: https://doi.org/10.1557/mrc.2019.26