Fig. 3
From: Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy
Feature extraction from simulated scanning X-ray nanoprobe diffraction microscopy measurement by conventional fitting and NanobeamNN. (a) Simulated spatial distribution of strains \(\upepsilon \left(\text{x},\text{y}\right)\) and tilts \(\upomega \left(\text{x},\text{y}\right)\), and \(\upchi \left(\text{x},\text{y}\right)\), which mimic spatially distributed features present in a scanning X-ray nanoprobe diffraction microscopy measurement, where there is one diffraction pattern for each unique pixel coordinate. (b) \(\upepsilon \left(\text{x},\text{y}\right)\), \(\upomega \left(\text{x},\text{y}\right)\), and \(\upchi \left(\text{x},\text{y}\right)\), as analyzed by conventional fitting of the correlation between measured and simulated diffraction. (c) \(\upepsilon \left(\text{x},\text{y}\right)\), \(\upomega \left(\text{x},\text{y}\right)\), and \(\upchi (\text{x},\text{ y})\), predicted by NanobeamNN.
