A Deep Convolutional Neural Network to Analyze Position Averaged Convergent Beam Electron Diffraction Patterns

TL;DR

This paper introduces a deep convolutional neural network system that automatically analyzes position averaged convergent beam electron diffraction patterns, enabling fast, accurate, and automated processing of large 4D STEM datasets.

Contribution

The paper presents a novel neural network approach that calibrates and measures sample parameters from diffraction patterns without preprocessing, significantly improving speed and automation over traditional methods.

Findings

Network processes patterns at ~0.1 s/pattern

Achieves accuracy comparable to brute force methods

Applicable to big 4D STEM data sets

Abstract

We establish a series of deep convolutional neural networks to automatically analyze position averaged convergent beam electron diffraction patterns. The networks first calibrate the zero-order disk size, center position, and rotation without the need for pretreating the data. With the aligned data, additional networks then measure the sample thickness and tilt. The performance of the network is explored as a function of a variety of variables including thickness, tilt, and dose. A methodology to explore the response of the neural network to various pattern features is also presented. Processing patterns at a rate of $\sim$0.1 s/pattern, the network is shown to be orders of magnitude faster than a brute force method while maintaining accuracy. The approach is thus suitable for automatically processing big, 4D STEM data. We also discuss the generality of the method to other materials/orientations as well as a hybrid approach that combines the features of the neural network with least squares fitting for even more robust analysis. The source code is available at https://github.com/subangstrom/DeepDiffraction.