Binary mask optimization for inverse lithography with partially coherent illumination

Recently, a set of generalized gradient-based optical proximity correction optimization methods have been developed to solve for the inverse lithography problem under coherent illumination. Most practical lithography systems, however, operate under partially coherent illumination. This paper focuses on developing gradient-based binary mask optimization methods that account for the inherent nonlinearities of partially coherent systems. Two nonlinear models are used in the optimization. The first relies on a Fourier representation that approximates the partially coherent system as a sum of coherent systems. The second model is based on an average coherent approximation that is computationally faster. To influence the solution patterns toward more desirable manufacturability properties, wavelet regularization is added to the optimization framework.