Pixel-based mask optimization via particle swarm optimization algorithm for inverse lithography

Lei Wang; Sikun Li; Xiangzhao Wang; Chaoxing Yang; Feng Tang

doi:10.1117/12.2230404

15 March 2016 Pixel-based mask optimization via particle swarm optimization algorithm for inverse lithography

Lei Wang, Sikun Li, Xiangzhao Wang, Chaoxing Yang, Feng Tang

Proceedings Volume 9780, Optical Microlithography XXIX; 97801V (2016) https://doi.org/10.1117/12.2230404
Event: SPIE Advanced Lithography, 2016, San Jose, California, United States

Abstract

An efficient pixel-based mask optimization method via particle swarm optimization (PSO) algorithm for inverse lithography is proposed. Because of the simplicity of principles, the ease of implementation and the efficiency of convergence, PSO has been widely used in many fields. In this study, PSO is used to solve the inverse problem of mask optimization. The pixel-based mask patterns are transformed into frequency space using discrete cosine transformation and the frequency components are encoded into particles. The pattern fidelity is adopted as the fitness function to evaluate these particles. The mask optimization method is implemented by updating the velocities and positions of these particles. Simulation results show that the image fidelity has been efficiently improved after using the proposed method.

Citation Download Citation

Lei Wang, Sikun Li, Xiangzhao Wang, Chaoxing Yang, and Feng Tang "Pixel-based mask optimization via particle swarm optimization algorithm for inverse lithography", Proc. SPIE 9780, Optical Microlithography XXIX, 97801V (15 March 2016); https://doi.org/10.1117/12.2230404

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