Retrieving the quantitative chemical information at nanoscale from SEM EDX measurements by Machine Learning

TL;DR

This paper introduces a machine learning approach using blind source separation and non-negative matrix factorization to accurately determine the chemical composition of nanostructures from SEM EDX measurements, validated by simulations and TEM data.

Contribution

The study presents a novel application of BSS and NMF to extract quantitative chemical information from SEM EDX spectra of nanostructures, enhancing analysis accuracy.

Findings

Successful decomposition of EDX spectra into source components

Validation of quantitative results with Monte Carlo simulations

Confirmation by cross-sectional TEM EDX measurements

Abstract

The quantitative composition of metal alloy nanowires on InSb(001) semiconductor surface and gold nanostructures on germanium surface is determined by blind source separation (BSS) machine learning (ML) method using non negative matrix factorization (NMF) from energy dispersive X-ray spectroscopy (EDX) spectrum image maps measured in a scanning electron microscope (SEM). The BSS method blindly decomposes the collected EDX spectrum image into three source components, which correspond directly to the X-ray signals coming from the supported metal nanostructures, bulk semiconductor signal and carbon background. The recovered quantitative composition is validated by detailed Monte Carlo simulations and is confirmed by separate cross-sectional TEM EDX measurements of the nanostructures. This shows that SEM EDX measurements together with machine learning blind source separation processing could be successfully used for the nanostructures quantitative chemical composition determination.