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

Inverse lithography technologies (ILTs) are critical for improving the imaging performance of lithography in advanced technology nodes. Pixel-based source optimization (SO), as an efficient part of ILTs, can be implemented via heuristic approaches to achieve high-performance lithographic imaging. In this paper, a SO approach based on a combination of the particle-swarm optimization and genetic algorithms (PSO–GA) is proposed to determine the optimal intensity distribution of the source via iterations. The pixelated source can be decoded into the optimized variables of the merit functions in the SO model. The proposed PSO–GA algorithm, as a high-efficiency hybrid algorithm, can transform the discrete SO problem into the optimal search solution for the merit function, thereby inversely enhancing the lithographic-imaging performance. In the forward-imaging model in the lithography, the extraction of the mask's effective diffraction spectrum is implemented to calculate the layout of resist patterns. The simulation results highlight the superior performance of the proposed approach in achieving pixelated SO over the traditional GA and PSO algorithm in terms of convergence capacity.