Virtual experiments for the assessment of data analysis and uncertainty quantification methods in scatterometry

Geometrical dimensions in nanostructures can be determined through indirect optical measurements carried out by a scatterometer. This includes solving a non-linear regression task in which a physical model is fitted to the observed optical diffraction pattern. We developed a virtual experiment which produces simulated diffraction patterns in coherent Fourier scatterometry measurements including perturbations due to various error sources. We utilize this virtual experiment to assess the suitability of data analysis and uncertainty quantification methods employed in scatterometry. In addition to investigating relevant physical parameters we explore the impact of deviations between the regression model utilized for the analysis and the scatterometry model used to produce the virtual diffraction pattern. We choose coverage probabilities of interval estimates of the geometrical dimensions as the main metric for the assessment. One of our findings is that the discretization level, expressed as the number of retained Fourier orders, can be relaxed up to order 9 in our case study, which is relevant as calculation times strongly depend on this parameter. Another result is that the least-squares approach considered here for solving the regression task in combination with the propagation of variances yields uncertainties which have somewhat lower coverage probabilities than the envisaged 95%. It turned out that it was critically important to model the oxide layer in order to get proper estimates of the width, or 'critical dimension', of the sample, while the uncertainty of the side wall angle had the largest impact on the uncertainty of the measurands. Our findings can help establishing traceability of Fourier scatterometry and underline the usefulness of high-quality virtual experiments in connection with complex measurement principles. At the same time the presented case study can be seen as a generic approach that could be followed for assessing uncertainty quantifications in other applications as well.