Thronsen E, Bergh T, Thorsen T I, Christiansen E F, Frafjord J, Crout P, van Helvoort A T J, Midgley P A, Holmestad R
Department of Physics, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway; Materials and Nanotechnology, SINTEF Industry, N-7465, Trondheim, Norway.
Department of Physics, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway; Department of Chemical Engineering, NTNU, N-7491 Trondheim, Norway.
Ultramicroscopy. 2024 Jan;255:113861. doi: 10.1016/j.ultramic.2023.113861. Epub 2023 Oct 6.
Mapping the spatial distribution of crystal phases with nm-scale spatial resolution is an important characterisation task in studies of multi-phase materials. One popular approach is to use scanning precession electron diffraction which enables semi-automatic phase mapping at the nanoscale by collecting a single precession electron diffraction pattern at every probe position over regions spanning up to a few micrometers. For a successful phase mapping each diffraction pattern must be correctly identified. In this work four different approaches for phase mapping of embedded precipitates in an Al-Cu-Li alloy are compared on a sample containing three distinct crystal phases. These approaches are based on: non-negative matrix factorisation, vector matching, template matching and artificial neural networks. To evaluate the success of each approach a ground truth phase map was manually created from virtual images based on characteristic phase morphologies and compared with the deduced phase maps. The percentage accuracy of all methods when compared to the ground truth was satisfactory, with all approaches obtaining scores above 98%. The optimal method depends on the specific task at hand. Non-negative matrix factorisation is suitable with limited prior data knowledge but performs best with few unique diffraction patterns and requires substantial post-processing. It has the advantage of reducing the dimensionality of the dataset and handles weak diffracted intensities well given that they occur repeatedly. The current vector matching implementation is fast, simple, based only on the Bragg spot geometry and requires few parameters. It does however demand that each Bragg spot is accurately detected in each pattern and the current implementation is limited to zone axis patterns. Template matching handles a large range of orientations, including off-axis patterns. However, achieving successful and reliable results often require thorough data pre-processing and do require adequate diffraction simulations. For artificial neural networks a substantial setup effort is demanded but once trained it excels for routine tasks, offering fast predictions. The implemented codes and the data used are available open-source. These resources and the detailed assessment of the methods will allow others to make informed decisions when selecting a data analysis approach for 4D-STEM phase mapping tasks on other material systems.
在多相材料研究中，以纳米级空间分辨率绘制晶体相的空间分布是一项重要的表征任务。一种常用的方法是使用扫描进动电子衍射，通过在跨越几微米的区域内的每个探针位置收集单个进动电子衍射图案，能够在纳米尺度上进行半自动相图绘制。为了成功进行相图绘制，每个衍射图案都必须被正确识别。在这项工作中，在一个包含三种不同晶体相的样品上，比较了四种不同的方法来绘制Al-Cu-Li合金中嵌入析出物的相图。这些方法基于：非负矩阵分解、向量匹配、模板匹配和人工神经网络。为了评估每种方法的成功程度，基于特征相形态从虚拟图像中手动创建了一个真实相图，并与推导的相图进行比较。与真实相图相比，所有方法的准确率都令人满意，所有方法的得分都在98%以上。最佳方法取决于手头的具体任务。非负矩阵分解在现有数据知识有限的情况下适用，但在独特衍射图案较少时表现最佳，并且需要大量的后处理。它具有降低数据集维度的优点，并且在弱衍射强度反复出现的情况下能够很好地处理。当前的向量匹配实现速度快、简单，仅基于布拉格斑点几何形状，并且需要很少的参数。然而，它确实要求在每个图案中准确检测每个布拉格斑点，并且当前实现仅限于晶带轴图案。模板匹配可以处理大范围的取向，包括离轴图案。然而，要获得成功和可靠的结果通常需要进行彻底的数据预处理，并且确实需要进行充分的衍射模拟。对于人工神经网络，需要大量的设置工作，但一旦训练完成，它在常规任务中表现出色，能够提供快速预测。所实现的代码和使用的数据都是开源的。这些资源以及对这些方法的详细评估将使其他人在为其他材料系统的4D-STEM相图绘制任务选择数据分析方法时能够做出明智的决策。
