2017/08/03 by Weizong Xu, James M. LeBeau · 80 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · Mathematics · Physics and Astronomy · #Advanced Electron Microscopy Techniques and Applications #Algorithm #Artificial intelligence #Artificial neural network #Computer science #Convolutional neural network #Deep learning #Diffraction #Electron and X-Ray Spectroscopy Techniques #Geometry #Machine Learning in Materials Science #Mathematics #Optics #Pattern recognition (psychology) #Physics #Position (finance) #Rotation (mathematics) #Tilt (camera) #cond-mat.mtrl-sci #physics.data-an
paper · pdf · doi:10.1016/j.ultramic.2018.03.004
published in Ultramicroscopy 188, 59-69 (Elsevier BV)
arxiv created 2017/08/03 · openalex publication_date 2018/03/05 · arxiv updated 2018/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.