Bayesian estimation for XPS spectral analysis at multiple core levels

Abstract

X-ray photoelectron spectroscopy (XPS) is a widely used measurement technique in material surface analysis, but its analysis is subject to operator arbitrariness in the results. In a previous paper, a method based on genetic algorithms was proposed to estimate the composition ratios of compounds from XPS data using reference spectra and it was shown that it is possible to analyze them automatically from the reference spectra data. In this paper, we newly proposed a Bayesian spectral decomposition method based on the exchange Monte Carlo method and tested it on artificial data. This method provides a posterior distribution of the model parameters. This not only allows the estimation of compositional ratios for samples, but also allows statistical reliability assessment. In addition, we simulated an artificial data analysis to clarify the effect on the identification of compounds and the estimation of their compositional ratios by varying the signal-to-noise ratio of the data.