Retrieving the Quantitative Chemical Information at Nanoscale from Scanning Electron Microscope Energy Dispersive X-ray Measurements by Machine Learning.

Abstract

The quantitative composition of metal alloy nanowires on InSb semiconductor surface and gold nanostructures on germanium surface is determined by blind source separation (BSS) machine learning method using non-negative matrix factorization 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 transmission electron microscopy EDX measurements of the nanostructures. This shows that simple and achievable SEM EDX measurements together with machine learning non-negative matrix factorization-based blind source separation processing could be successfully used for the nanostructures quantitative chemical composition determination. Our finding can make the chemical quantification at the nanoscale much faster and cost efficient for many systems.