2019 年 17 巻 p. 61-68
In this study, we introduce a novel algorithm that can recognize the concavo-convex shapes of X-ray photoelectron spectroscopy (XPS) data and estimate the optimum background (BG) in XPS spectra with fine structures near the endpoints. In this algorithm, the active Shirley method was improved by incorporating a function for automatically selecting the analytical range. This autoselection function first investigates all the candidates for the initial endpoints. These estimates are then used to decide the BG shape according to the Shirley method. In order to exclude false-positive candidates caused by the recognition of noise peaks as small XPS peaks, the function evaluates the concavo-convex shape of the XPS spectrum after the long-period noise is removed using a smoothing process. The proposed algorithm was demonstrated to successfully estimate the optimal spectral BG from an XPS spectrum with a poor signal-to-noise ratio of about 40%.
