"极紫外光刻像素化掩模优化方法：双重边界演化策略"

Extreme ultraviolet lithography (EUVL) is a cutting-edge technology used in semiconductor manufacturing, which requires highly precise mask optimization for improved imaging fidelity. In this paper, a fast pixelated mask optimization method for EUVL is proposed, aiming to enhance the efficiency and accuracy of mask image simulation. The optimization process involves the utilization of an improved pixelated thick mask model, where the size of point pulses on edge pixels is determined according to the mask pixel size. Additionally, a dual-boundary evolution method is employed, which adaptively initializes the optimization variables based on the difference between the current lithographic resist contour and the target pattern contour. This approach leverages prior knowledge to generate initial individuals and populations, thereby boosting optimization efficiency. To validate the effectiveness of the proposed method, simulations were conducted using one-dimensional line-space patterns and two-dimensional complex shapes. The results demonstrate that the optimization method significantly enhances mask image simulation accuracy and improves the efficiency of optimizing two-dimensional mask patterns. Overall, the fast pixelated mask optimization method presented in this paper offers a promising solution for enhancing the performance of EUVL technology. By leveraging advanced modeling techniques and adaptive optimization strategies, this approach shows great potential for advancing mask optimization in semiconductor manufacturing processes. The optimized masks generated through this method have the potential to improve imaging fidelity, ultimately contributing to the development of more advanced and efficient semiconductor devices.