Potapov Pavel, Lubk Axel
Leibniz Institute for Solid State and Materials Research (IFW), Dresden, Germany.
Micron. 2021 Jun;145:103068. doi: 10.1016/j.micron.2021.103068. Epub 2021 Apr 8.
This article addresses extraction of physically meaningful information from STEM EELS and EDX spectrum-images using methods of Multivariate Statistical Analysis. The problem is interpreted in terms of data distribution in a multi-dimensional factor space, which allows for a straightforward and intuitively clear comparison of various approaches. A new computationally efficient and robust method for finding physically meaningful endmembers in spectrum-image datasets is presented. The method combines the geometrical approach of Vertex Component Analysis with the statistical approach of Bayesian inference. The algorithm is described in detail at an example of EELS spectrum-imaging of a multi-compound CMOS transistor.
本文介绍了使用多元统计分析方法从STEM-EELS和EDX光谱图像中提取具有物理意义的信息。该问题从多维因子空间中的数据分布角度进行解释，这使得对各种方法进行直接且直观清晰的比较成为可能。本文提出了一种在光谱图像数据集中寻找具有物理意义的端元的新的计算高效且稳健的方法。该方法将顶点成分分析的几何方法与贝叶斯推理的统计方法相结合。以多化合物CMOS晶体管的EELS光谱成像为例详细描述了该算法。
