Automated analysis of photoelectron yield spectroscopy spectra interpreted via power laws

We propose an automated method for threshold analysis in photoelectron yield spectroscopy (PYS), a technique used for measuring the ionization energy of inorganic and organic semiconductors. PYS spectra can be interpreted using the power law. First, the analysis region, the region of interest (ROI), within the measured spectrum must be identified and isolated for power law interpretation. Segment regression is employed to estimate the change point of the spectrum and slope on either side. The ROI is extracted using an algorithm that accounts for the slope variation (PW-ROI method). The inverse of the power number (n) derived from the physical model is then applied to the spectrum, and fitting is performed using the mean absolute error as the minimization function with the extended rectified linear unit function (1/n plot method). In cases with multiple candidate models, the optimal model is chosen based on the coefficient of determination using the 1/n plot method. This automated analysis method significantly reduces the processing time and ensures that the values are objective and human independent. Results of automated analysis closely align with those of manual analysis, with their differences limited within the range of ± 0.1 eV.