  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.
