TOF-SIMS Image Data Fusion by Multivariate Analysis and TOF-SIMS Spectrum Analysis by Sparse Modeling and Machine Learning

Abstract

Time-of-Flight secondary ion mass spectrometry (TOF-SIMS) and scanning electron microscope (SEM) images were fused and then evaluated by means of principal component analysis. As a result, TOF-SIMS spatial resolution could be improved by adding SEM image information to TOF-SIMS data without drastic change of TOF-SIMS spectrum information. Sparse modeling and machine learning were applied to TOF-SIMS data to interpret complex TOF-SIMS spectra. Least Absolute Shrinkage and Selection Operator (LASSO) provided a simplified TOF-SIMS spectrum with less noise. Machine learning using Random Forest and k-Nearest Neigbour appropriately predicted unknown test samples by learning TOF-SIMS data similar the test samples.