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STEM能谱X射线能谱图像的最优主成分分析

Optimal principal component analysis of STEM XEDS spectrum images.

作者信息

Potapov Pavel, Lubk Axel

机构信息

1Department of Physics, Technical University of Dresden, Dresden, Germany.

2Leibniz Institute for Solid State and Materials Research (IFW), Dresden, Germany.

出版信息

Adv Struct Chem Imaging. 2019;5(1):4. doi: 10.1186/s40679-019-0066-0. Epub 2019 Apr 9.

DOI:10.1186/s40679-019-0066-0
PMID:31032174
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6456488/
Abstract

STEM XEDS spectrum images can be drastically denoised by application of the principal component analysis (PCA). This paper looks inside the PCA workflow step by step on an example of a complex semiconductor structure consisting of a number of different phases. Typical problems distorting the principal components decomposition are highlighted and solutions for the successful PCA are described. Particular attention is paid to the optimal truncation of principal components in the course of reconstructing denoised data. A novel accurate and robust method, which overperforms the existing truncation methods is suggested for the first time and described in details.

摘要

通过应用主成分分析（PCA），STEM XEDS光谱图像可以得到显著去噪。本文以一个由多个不同相组成的复杂半导体结构为例，逐步深入研究PCA工作流程。文中突出了扭曲主成分分解的典型问题，并描述了成功进行PCA的解决方案。在重建去噪数据的过程中，特别关注主成分的最优截断。首次提出并详细描述了一种新颖、准确且稳健的方法，该方法优于现有的截断方法。

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