A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns

•A deep convolutional neural network is established to measure PACBED parameters including size, center, rotation, thickness and tilt.•The convolutional neural network automatically analyzes PACBED patterns at a rate of 0.1 s/pattern, orders of magnitude faster than brute force methods.•The method is demonstrated to be well suited for big data analysis.•A methodology to evaluate the convolutional neural network response is proposed and explored.•A convolutional neural network/least squares hybrid method is proposed for further generalization and to accelerate processing. 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 ∼ 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.