Bruefach Alexandra, Ophus Colin, Scott Mary C
Department of Materials Science and Engineering, University of California, Berkeley, CA 94720, USA.
National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA.
Microsc Microanal. 2022 Sep 8:1-11. doi: 10.1017/S1431927622012259.
Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.
了解材料结构对于设计高性能的工程器件和材料至关重要。四维扫描透射电子显微镜（4D-STEM）能够在微米级视场上绘制纳米级局部晶体结构。然而，4D-STEM数据集可能包含来自各种材料结构的数万张图像，这使得结构的自动检测和分类变得困难。传统的4D-STEM自动分析流程侧重于监督方法，这种方法需要材料结构的先验知识，并且无法描述异常或偏离的结构。在本文中，介绍了一种使用非负矩阵分解（NMF）对无监督聚类的4D-STEM特征表示进行工程处理的流程。使用NMF对每个特征进行评估，并给出了模拟数据和实验数据的结果。结果表明，一些数据表示能够更可靠地识别重叠晶粒。此外，应用实空间细化来识别空间上不同的样品区域，从而可以进行尺寸和形状分析。这项工作为使用4D-STEM改进对偏离预期晶体排列的材料中的纳米级结构特征的分析奠定了基础。
