Fast heuristic-based source mask optimization for EUV lithography using dual edge evolution and partial sampling

Abstract

Extreme ultraviolet (EUV) lithography is essential in the advanced technology nodes. Source mask optimization (SMO) for EUV lithography, especially the heuristic-based SMO, is one of the vital resolution enhancement techniques (RET). In this paper, a fast SMO method for EUV based on dual edge evolution and partial sampling strategies is proposed to improve the optimization efficiency and speed of the heuristic algorithm. In the source optimization (SO) stage, the position and intensity of the source points are optimized in turn. Using the sparsity of the optimized source, a partial sampling encoding method is applied to decrease the variables’ dimension in optimization. In the mask optimization (MO) stage, the main features (MF) and the sub-resolution assistant features (SRAF) are optimized in turn. A dual edge evolution strategy is used in the MF optimization and the partial sampling encoding method is used in SRAF optimization. Besides, the imaging qualities at different focal planes are improved by SRAF optimization. The optimization efficiency is greatly improved by the dimensionality reduction strategies. Simulations are carried out with various target patterns. Results show the superiority of the proposed method over the previous method, especially for large complex patterns.