Extreme ultraviolet lithography (EUV) is a key technology for micro-nano processing and is widely used in the chip manufacturing process. Mask optimization is one of the key resolution enhancement techniques in EUV lithography. In this paper, a thick mask optimization method based on particle swarm optimization (PSO) algorithm is proposed to improve simulation accuracy and imaging quality. In this work, we change the calculation order of formulas, which is used to accelerate the imaging calculations. The equivalent film layer method is used to approximate the reflection coefficients of thick mask multilayer film structures to improve the simulation accuracy. The inverse lithography problem for thick mask optimization is solved by particle swarm optimization algorithm. The simulation results show that this method can effectively improve the simulation accuracy and imaging quality.
Extreme ultra-violet (EUV) lithography photomask defects are a common problem in the lithography printing process, which has a serious impact on the lithography printing process. Therefore, it is necessary to detect and quickly locate the defect. Many researchers have used image processing and machine learning methods to quickly identify defects in EUV photomasks and subsequently repair them. This paper proposes a detection method based on neural network image segmentation, and we introduce an improved U-Net to predict photomask defects. Our experiments show that the network model has better accuracy. In the process of identifying the defect image, it is in good agreement with the ground truth.
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