Electron backscatter diffraction is a widely used technique for nano- to micro-scale analysis of crystal structure and orientation. Backscatter patterns produced by an alloy solid solution matrix and its ordered superlattice exhibit only extremely subtle differences, due to the inelastic scattering that precedes coherent diffraction. We show that unsupervised machine learning (with principal component analysis, non-negative matrix factorisation, and an autoencoder neural network) is well suited to fine feature extraction and superlattice/matrix classification. Remapping cluster average patterns onto the diffraction sphere lets us compare Kikuchi band profiles to dynamical simulations, confirm the superlattice stoichiometry, and facilitate virtual imaging with a spherical solid angle aperture. This pipeline now enables unparalleled mapping of exquisite crystallographic detail from a wide range of materials within the scanning electron microscope.

中文翻译：

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球面角暗场成像和敏感微结构相位聚类与无监督机器学习

电子背散射衍射是一种广泛用于纳米到微米级晶体结构和取向分析的技术。由于相干衍射之前的非弹性散射，合金固溶体基质及其有序超晶格产生的背向散射图案仅表现出极其细微的差异。我们表明无监督机器学习（具有主成分分析、非负矩阵分解和自动编码器神经网络）非常适合精细特征提取和超晶格/矩阵分类。将簇平均图案重新映射到衍射球上，让我们将菊池带轮廓与动力学模拟进行比较，确认超晶格化学计量，并促进具有球面立体角孔径的虚拟成像。