Improved reconstruction of critical dimensions in extreme ultraviolet scatterometry by modeling systematic errors

Scatterometry is a non-imaging indirect optical method that is frequently used to reconstruct the critical dimensions (CD) of periodic nanostructures, e. g. structured wafer surfaces in semiconductor chip production. To solve the inverse problem, we apply a maximum likelihood estimation, introduced in Henn et al (2012 Opt. Express 20 12771-86). Along with the CD values, further relevant quantities like noise parameters of the measured diffraction intensities and the strength of line roughness can be estimated from the measured scattering efficiencies. We investigate three different models for extreme ultraviolet (EUV) scatterometry at an EUV photo mask with increasing complexity by successively including two major sources of systematic errors, namely line roughness and deviations in the multilayer substrate of the EUV mask. Applying the different models to reconstruct the CDs from both simulation and measurement data, we demonstrate the improvements of the reconstruction in terms of simulated and real measurement data. The inclusion of systematic errors in the maximum likelihood approach to the inverse problem leads to a significant reduction of the variances in the estimated CDs implying reduced measurement uncertainty for scatterometry.