Automatic estimation of unknown chemical components in a mixed material by XPS analysis using a genetic algorithm

Abstract

There is an urgent need to develop automatic analysis methods for the large number of X-ray photoelectron spectroscopy (×PS) spectra being obtained by methods such as 3D chemical analysis and operand analysis. In a previous study, we developed an automatic analysis method for mixed materials that can decompose the XPS spectra and estimate the compositional ratios by comparison with XPS reference spectra of many candidate single-phase compounds. This method needs access to the XPS reference spectrum of every possible compound in the sample. However, in many practical cases, the necessary XPS reference spectra are not always available. In this study, we developed an automatic analysis method to estimate the compositional ratios, even when all necessary XPS reference spectra are not available, i.e. the measured XPS spectra contain unknown peak structures. In particular, the new method can automatically estimate the number of unknown peaks by the combination of a genetic algorithm and the Bayesian information criterion. We applied the method to analyze the depth-resolved XPS spectra of a $${\rm{Pb(Zr,Ti)}}{{\rm{O}}_{\rm{3}}}$$ (PZT) piezoelectric film and successfully identified the change in the chemical states of the components in the film without ambiguity.

Keywords

• x-ray photo-electron spectroscopy
• automated spectral data analysis
• genetic algorithm