Extraction of physically meaningful endmembers from STEM spectrum-images combining geometrical and statistical approaches

TL;DR

This paper introduces a new computational method that combines geometrical and statistical techniques to extract physically meaningful endmembers from STEM spectrum-images, demonstrated on CMOS transistor data.

Contribution

A novel, efficient, and robust method integrating Vertex Component Analysis with Bayesian inference for spectrum-image endmember extraction.

Findings

Effective extraction of meaningful endmembers demonstrated on EELS spectrum-imaging data.

Method outperforms traditional approaches in robustness and computational efficiency.

Provides clear comparison framework for different analytical approaches.

Abstract

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.