基于动态适应度函数的光源掩模优化方法

提出了一种基于动态适应度函数的光刻机光源掩模优化方法(SMO)。动态适应度函数方法在遗传算法优化过程中采用动态适应度函数模拟真实光刻工艺条件误差对光刻结果的影响，得到对光刻工艺条件误差不敏感的优化光源和优化掩模。该方法无需优化权重系数，即可获得与权重优化后的加权适应度函数方法相近的工艺宽容度。典型逻辑图形的仿真实验表明，曝光剂量误差为15%时，动态适应度函数方法得到的优化光源和优化掩模的可用焦深达到200 nm，与加权适应度函数方法的优化效果相当。动态适应度函数方法也可用于降低SMO 的优化光源和掩模对其他工艺条件误差如彗差的敏感度。

Abstract

A dynamic source mask optimization (SMO) method is developed. The dynamic SMO method uses a dynamic fitness function in genetic algorithm to simulate the process variations in real lithography process. So the imaging quality of the optimized source and mask is not sensitive to the process errors. The dynamic SMO method can get similar result as the conventional weighted SMO method without the necessity of weighting coefficient optimization. Simulation results show that the dynamic method can get a usable defocus of 200 nm when the dose error is 15%. This is comparable with the optimized result of the weighted method. The dynamic SMO method can be also used to make the optimized source and mask less sensitive to other process errors, such as coma errors.

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杨朝兴, 李思坤, 王向朝. 基于动态适应度函数的光源掩模优化方法[J]. 光学学报, 2016, 36(1): 0111006. Yang Chaoxing, Li Sikun, Wang Xiangzhao. Source Mask Optimization Based on Dynamic Fitness Function[J]. Acta Optica Sinica, 2016, 36(1): 0111006.

基于动态适应度函数的光源掩模优化方法

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