Methodology and practical application of an ArF resist model calibration

This paper focuses on a novel methodology for a fast and efficient resist model calibration. One of the most crucial parts when calibrating a resist model is the fitting of experimental data where up to 20 resist model parameters are varied. Although general optimization approaches such as simplex algorithms or genetic algorithms have proven suitable for many applications, they do not exploit specific properties of resist models. Therefore, we have developed a new strategy based on Design of Experiment methods which makes use of these specific characteristics. This algorithm will be outlined and then be demonstrated by applying it to the calibration of a Solid-C resist model for one ArF line/space resist. As characterizing dataset we chose: a) a Focus Exposure Matrix (FEM) for the dense array, b) linearity, c) OPE (optical proximity) curve and e) the MEEF (mask error enhancement factor) for a dense array. It turned out that a simultaneous fit of the complete data set wa s not possible by varying resist parameters only. Considering the optical parameters appeared to be crucial as well. Therefore the influence of the optical settings (illumination, projection, 3D mask effects) on the lithography process will be discussed at this point. Finally we obtained an excellent matching of model predictions and experimental results.