Efficient Removal of Noise-derived Components for Automatic XPS Spectral Decomposition Using Hierarchical Clustering

抄録

In this paper, we aim to automatically provide a solution to peak separation in an X-ray photoelectron spectroscopy (XPS) spectrum with non-negligible statistical noise that is inevitably accepted in multi-dimensional (e.g., 2-dimensional/3-dimensional XPS profiles) XPS measurement. To achieve this, in our previous study [H. Shinotsuka et al., J. Electron Spectros. Relat. Phenomena 239, 146903 (2020)], we automatically selected optimal solutions using the Bayesian information criterion (BIC) for measured XPS spectra. This was successfully performed for many varieties of XPS spectra. However, the optimal solution rarely included a small and sharp peak that was likely to be caused by statistical noise. In this study, we investigate a practical method to eliminate the infrequent solution with a noise-derived peak. This method uses hierarchical clustering with peak parameters (i.e., width and area) as a preprocessing step before selecting the solutions using the BIC.