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Researchers from the Shanghai Institute of Optics and Fine Mechanics of the Chinese Academy of Sciences proposed a source mask optimization (SMO) technique for extreme-ultraviolet lithography (EUVL) based on a thick mask model and a social learning particle swarm optimization (SL-PSO) algorithm.
Simulations of the method suggested that the technique is more effective than similar approaches based on heuristic algorithms in optimization efficiency.
With the continuous shrinking of the critical dimension of integrated circuits, the researchers said, precision and optimal performance requirements have become more stringent. Computational lithography improves performance without changing the hardware or software configurations of…READ MORE