We present results in the final part of a three-part of a three-part study employing standard test data (STD) to estimate errors in peak parameters derived from data analysis procedures used in x-ray photoelectron spectroscopy (XPS). XPS-STD are simulated doublet and singlet XP spectra based on spline polynomial models of measures C 1s spectra. The XPS-STD contained 10 sets of spectra, each of which consisted of 18 doublets and 4 singlets. The doublet spectra in each set were created from a replicated factorial design with variation in peak separation, component peak intensity and amount of Poisson noise. We statistically analyze errors in binding energies and intensities by the type of curve-fitting approach using the Kruskal-Wallis test and the Mann-Whitney U-test. Bias was used to measure the accuracy of a curve-fitting approach, while random error was used to measure the precision of the approach. Seven curve-fitting approaches were used by a group of 20 analysts to represent single peaks: a single Gaussian (G), a Gaussian-Lorentzian (G-L), a Voigt function, a G or G-L functions. Despite the fact that individual peaks were nearly symmetrical, curve-fitting approaches that accounted for peak asymmetry proved to be the most accurate for determining both peak intensities and binding energies. For the doublets exhibiting the largerpeak at the higher binding energy, the use of dual G-L functions to fit individual peaks, as a way to account for peak asymmetry, produced the highest accuracy in determining peak binding energy. This dual peak-shape approach for this particular spectral condition is also preferable for determining peak intensities and produces better precision. The XPS-STD and the statistical analysis methods presented here provide a means to distinguish differences in the accuracy of various curve-fitting approaches for spectral conditions that resemble those of the XPS-STD. To obtain the XPS-STD and to have on online evaluation of curve-fitting accuracy and precision, consult the following web site: http://www.act.nist.gov/std.
Citation: Surface and Interface Analysis
Issue: No. 12
Pub Type: Journals
accuracy, algorithm testing, bias, curve fitting, precision, random error, reference data