Deep Learning of Structural Morphology Imaged by Scanning X-ray Diffraction Microscopy

TL;DR

This paper introduces NanobeamNN, a convolutional neural network that rapidly and accurately analyzes nanoscale strain and rotation in X-ray diffraction microscopy data, overcoming computational challenges of traditional methods.

Contribution

The study presents a novel neural network approach for analyzing complex diffraction data, enabling faster and potentially more accurate strain and rotation measurements.

Findings

NanobeamNN significantly speeds up data analysis.

It accurately predicts lattice strain and rotations from simulated data.

The method performs well on experimental data without fine-tuning.

Abstract

Scanning X-ray nanodiffraction microscopy is a powerful technique for spatially resolving nanoscale structural morphologies by diffraction contrast. One of the critical challenges in experimental nanodiffraction data analysis is posed by the convergence angle of nanoscale focusing optics which creates simultaneous dependency of the far-field scattering data on three independent components of the local strain tensor - corresponding to dilation and two potential rigid body rotations of the unit cell. All three components are in principle resolvable through a spatially mapped sample tilt series however traditional data analysis is computationally expensive and prone to artifacts. In this study, we implement NanobeamNN, a convolutional neural network specifically tailored to the analysis of scanning probe X-ray microscopy data. NanobeamNN learns lattice strain and rotation angles from simulated diffraction of a focused X-ray nanobeam by an epitaxial thin film and can directly make reasonable predictions on experimental data without the need for additional fine-tuning. We demonstrate that this approach represents a significant advancement in computational speed over conventional methods, as well as a potential improvement in accuracy over the current standard.