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。 A light source mask optimization method (SMO) based on dynamic fitness function is proposed. The dynamic fitness function method uses the dynamic fitness function in the optimization process of genetic algorithm to simulate the influence of the error of the actual photolithographic process conditions on the lithography results and obtain the optimized light source and the optimized mask insensitive to the error of lithography process conditions. The method does not need to optimize the weighting coefficient, and obtains the process latitude similar to the weighted fitness function method after weight optimization. Simulation results of typical logic graphs show that the available depth of focus of the optimized light source and the optimized mask obtained by the dynamic fitness function method reaches 200 nm when the exposure dose error is 15%, which is equal to the optimization effect of the weighted fitness function method. Dynamic fitness function methods can also be used to reduce the sensitivity of SMO’s optimized light sources and masks to other process conditions such as coma.