Probabilistic parameter estimation using a Gaussian mixture density network: application to X‐ray reflectivity data curve fitting

X‐ray reflectivity (XRR) is widely used for thin‐film structure analysis, and XRR data analysis involves minimizing the difference between experimental data and an XRR curve calculated from model parameters describing the thin‐film structure. This analysis takes a certain amount of time because it involves many unavoidable iterations. However, the recently introduced artificial neural network (ANN) method can dramatically reduce the analysis time in the case of repeated analyses of similar samples. Here, the analysis of XRR data using a mixture density network (MDN) is demonstrated, which enables probabilistic prediction while maintaining the advantages of an ANN. First, under the assumption of a unimodal probability distribution of the output parameter, the trained MDN can estimate the best‐fit parameter and, at the same time, estimate the confidence interval (CI) corresponding to the error bar of the best‐fit parameter. The CI obtained in this manner is similar to that obtained using the Neumann process, a well known statistical method. Next, the MDN method provides several possible solutions for each parameter in the case of a multimodal distribution of the output parameters. An unsupervised machine learning method is used to cluster possible parameter sets in order of probability. Determining the true value by examining the candidates of the parameter sets obtained in this manner can help solve the inherent inverse problem associated with scattering data. A mixture density network (MDN), a neural network method that can make probabilistic predictions, is applied to X‐ray reflectivity data analysis. The probability distribution of several possible parameters obtained using an MDN can help estimate the confidence interval and solve the inverse problem.