Classifying Grazing Incidence X-ray Scattering Patterns via Convolutional Neural Networks

Nano-structured thin films have a variety of applications, such as antireflecting coatings for solar cells, waveguides, gaseous sensors, and piezoelectric devices. Grazing-incidence small-angle X-ray scattering (GISAXS) has become a key technique to determine the morphologies of such thin films. One of the main challenges is to determine the structure information encoded in the data based on scattering patterns alone. We propose a computational scheme that learns the structure of well-defined layers of nanoparticles from GISAXS patterns. We explore this class of thin-film materials in terms of physics-based simulation models and experimental data and apply convolutional neural networks to the simulated data to obtain the encoded information of the morphology. Our classification models categorize millions of simulated scattering patterns with success rates over 94%. In addition, we show how these data-driven models have the potential to decrease analysis time of real scattering patterns from experim