18 March 2015 Source optimization using particle swarm optimization algorithm in photolithography
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Abstract
In recent years, with the availability of freeform sources, source optimization has emerged as one of the key techniques for achieving higher resolution without increasing the complexity of mask design. In this paper, an efficient source optimization approach using particle swarm optimization algorithm is proposed. The sources are represented by pixels and encoded into particles. The pattern fidelity is adopted as the fitness function to evaluate these particles. The source optimization approach is implemented by updating the velocities and positions of these particles. The approach is demonstrated by using two typical mask patterns, including a periodic array of contact holes and a vertical line/space design. The pattern errors are reduced by 66.1% and 39.3% respectively. Compared with the source optimization approach using genetic algorithm, the proposed approach leads to faster convergence while improving the image quality at the same time. The robustness of the proposed approach to initial sources is also verified.
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Lei Wang, Lei Wang, Sikun Li, Sikun Li, Xiangzhao Wang, Xiangzhao Wang, Guanyong Yan, Guanyong Yan, Chaoxing Yang, Chaoxing Yang, } "Source optimization using particle swarm optimization algorithm in photolithography", Proc. SPIE 9426, Optical Microlithography XXVIII, 94261L (18 March 2015); doi: 10.1117/12.2181335; https://doi.org/10.1117/12.2181335
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