Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy.

Area of Science:

• Materials Science
• Condensed Matter Physics
• Nanotechnology

Background:

• Scanning X-ray nanodiffraction microscopy (SXNM) spatially resolves nanoscale structures using diffraction contrast.
• SXNM data analysis is challenged by the convergence angle of focusing optics, coupling strain and rotation information.
• Conventional analysis methods are computationally intensive and susceptible to artifacts.

Purpose of the Study:

• To develop a computationally efficient and accurate method for analyzing SXNM data.
• To address the challenge of deconvoluting strain and rotation components in SXNM.
• To implement a machine learning approach for accelerating nanodiffraction data interpretation.

Main Methods:

• Implementation of NanobeamNN, a convolutional neural network tailored for SXNM data.
• Training NanobeamNN on simulated diffraction data of epitaxial thin films.
• Direct application of NanobeamNN to experimental SXNM data without fine-tuning.

Main Results:

• NanobeamNN effectively learns lattice strain and rotation angles from simulated data.
• The network demonstrates reasonable predictive performance on experimental SXNM data.
• Significant advancements in computational speed compared to traditional analysis methods were achieved.

Conclusions:

• NanobeamNN offers a substantial improvement in computational speed for SXNM data analysis.
• The approach shows potential for enhanced accuracy over current standard methods.
• This AI-driven technique accelerates the understanding of nanoscale structural properties.