Efficient removal of noise-derived components for automatic XPS spectral decomposition using hierarchical clustering

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, 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. (author)