Title: Retrieving the quantitative chemical information at nanoscale from SEM EDX measurements by Machine Learning,
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.