Stepwise fitting methodology for optical proximity correction modeling

Abstract

Optical proximity correction (OPC) models consist of a large number of components and parameters that must be optimized during model fitting process for best possible matching with empirical data. There are several optimization methods for OPC models. Most of the published methods, if not all, are based on a global optimization method, where all the model parameters are regressed in their search regions to provide a global minimum of the OPC model. However, there are potential risks of overweighting one OPC model component versus another and as a result loosing the physicality of the final model, which reduces model quality in terms of fit and prediction. In this work a stepwise fitting methodology based on staged optimization of the OPC model components is presented. Components are added into an OPC model in the order of more physical to less physical, starting from mask and optics. In each optimization stage a component is optimized using global regression methods and then the optimized parameters are locked and not regressed during further model optimization. The effectiveness of this approach in terms of accurate correction and comparison with global search regression method is demonstrated through computational experiments.