A step toward calculating the uncertainties in combined GIXRF‐XRR

Abstract The combination of X‐ray reflectivity (XRR) and grazing incidence X‐ray fluorescence (GIXRF) is a surface sensitive analytical method, which can be used for the characterization of thin films and multilayered materials. Both of these techniques are implemented on the same experimental setup and make use of similar mechanical processes and the same fundamental physical concept required for a combined data analysis. The combination of these techniques removes ambiguous results for the characterization of nanometer layers, as well as nanometer depth profiles, resulting in more accurate characterization of thickness, roughness, density, and elemental composition. Due to the vast number of fitting parameters, the estimation of the thin film sample structure is a challenging task. In this paper, we propose a recursive method for estimating the uncertainties of data from GIXRF‐XRR analysis, based on a Bootstrap statistical method. This approach relies on re‐sampling a dataset to estimate statistics on a population by applying random weights. We applied this method on an as‐deposited chalcogenide germanium, antimony, and tellurium (GST) thin film with a carbon‐capping layer. We found good agreement between the experimental and the theoretical XRR‐GIXRF values for a sample structure model, of which the parameters were determined within a confidence interval using the bootstrap method. We also propose an approach for calculating the uncertainty on the solid angle of detection based on Monte Carlo simulations.