Retrieving the quantitative chemical information at nanoscale from SEM EDX measurements by Machine Learning
arXiv:1705.00081 · doi:10.1021/acs.nanolett.7b01789
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.
References in corpus (1)
Cited by in corpus (9)
- Machine learning in nuclear materials research
- Artificial Intelligent Atomic Force Microscope Enabled by Machine Learning
- Automatic microscopic image analysis by moving window local Fourier Transform and Machine Learning
- Designing Materials Acceleration Platforms for Heterogeneous CO2 Photo(thermal)catalysis
- Charting the low-loss region in Electron Energy Loss Spectroscopy with machine learning
- Towards Understanding of Gold Interaction with AIII-BV Semiconductors at Atomic Level
- Into the Origin of Electrical Conductivity for the Metal-Semiconductor Junction at the Atomic Level
- Nanostructure phase and interface engineering via controlled Au self-assembly on GaAs(001) surface
- Evaluating Metal-Organic Precursors for Focused Ion Beam Induced Deposition through Solid-Layer Decomposition Analysis