Sampling-based imaging model for fast source and mask optimization in immersion lithography

Abstract

Current source and mask optimization (SMO) research tends to focus on advanced inverse optimization algorithms to accelerate SMO procedures. However, innovations of forward imaging models currently attract little attention, which impacts computational efficiency more significantly. A sampling-based imaging model is established with the innovation of an inverse point spread function to reduce computational dimensions, which can provide an advanced framework for fast inverse lithography. Simulations show that the proposed SMO method with the help of the proposed model can further speed up the algorithm-accelerated SMO procedure by a factor of 3.

Inverse Lithography Source Optimization via Particle Swarm Optimization and Genetic Combined Algorithm

“…Wang et al employed PSO to evaluate the intensity distribution of the source, in which pattern fidelity was adopted as the fitness function to evaluate the simulation results [20]. In research works, heuristic algorithms have been proven to exhibit considerable potential for improving the performance of lithographic imaging [30], [35]- [37]. Nevertheless, the existing heuristic algorithms that are generally applied in optimization models make it difficult to deviate from the local optimum in the case of a complex merit function [38], [39].…”