High-throughput XPS spectrum modeling with autonomous background subtraction for 3d5/2 peak mapping of SnS

Abstract

We propose a fitting model that automatically conducts the background subtraction during high-throughput peak fitting. The fitting model consists of the pseudo-Voigt mixture model and the ramp-sum background model, and each model represents the peak and background component, respectively. The optimization of the fitting model is performed by the spectrum adapted ECM algorithm that enables us to perform the peak fitting and background subtraction simultaneously through the high-throughput calculation. Application of the proposed model to the synthetic spectral data showed appropriate decomposition of the peak and background component. We also applied the proposed model to 3721 spectral data collected from the SnS sheet by X-ray photoelectron spectroscopy. The spectral data from the SnS sheet were successfully decomposed to the component of Sn $$3{d_{3/2}}$$ peak, Sn $$3{d_{5/2}}$$ peak and background, respectively. As the spectrum adapted ECM algorithm can efficiently analyze a large amount of spectral data, we can obtain the color map showing spatial distribution of Sn(II) and Sn(IV) using the parameter of Sn $$3{d_{5/2}}$$ peak. The proposed model supports us to obtain the insightful spatial distribution of peak component that has been difficult to obtain by conventional peak fitting.

Keywords

• peak fitting
• background subtraction
• x-ray photoelectron spectroscopy
• expectation-conditional maximization algorithm
• high-throughput analysis