Optimization of defect compensation for extreme ultraviolet lithography mask by covariance-matrix-adaption evolution strategy

摘要

Background: Defect compensation is one of the enabling techniques for high-volume manufacturing using extreme ultraviolet lithography. Aim: The advanced evolution strategy algorithm based on covariance matrix adaption is applied to compensation optimization to improve the convergence efficiency and algorithm operability. Approach: The advanced algorithm optimizes the solution population by sampling from the self-adapted covariance matrix of mutation distribution. Results: Optimization simulations for three different masks validated the algorithm's advantage in convergence efficiency and searching ability compared with original differential evolution, evolution strategy, genetic algorithm (GA), and Nelder-Mead simplex method. The advanced algorithm employs fewer user-defined parameters and is proved to be robust to variations of these parameters. Conclusions: The advanced algorithm obtains better results compared with GA for best-focus, through-focus, and complex-pattern optimizations. With the inherent invariance property, appropriate operability, and robustness, we recommend applying this algorithm to other lithography optimization problems.