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.

中文翻译：

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STEM XEDS光谱图像的最佳主成分分析。

可以通过应用主成分分析（PCA）对STEM XEDS光谱图像进行大幅消噪。本文逐步研究了PCA工作流程内部的复杂半导体结构的示例，该结构由许多不同的阶段组成。突出了扭曲主成分分解的典型问题，并描述了成功PCA的解决方案。在重建去噪数据的过程中，应特别注意主成分的最佳截断。首次提出了一种优于现有截断方法的新颖，精确且鲁棒的方法，并对其进行了详细说明。