Optimal principal component Analysis of STEM XEDS spectrum images

TL;DR

This paper improves denoising of STEM XEDS spectrum images using PCA by analyzing the workflow, identifying common issues, and introducing a novel, more accurate method for optimal component truncation to enhance data reconstruction.

Contribution

It provides a detailed analysis of PCA application to spectrum images and introduces a new robust truncation method that outperforms existing techniques.

Findings

New truncation method outperforms existing ones

Enhanced denoising of complex spectrum images

Improved accuracy in data reconstruction

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.