基于多染色体遗传算法的像素化光源掩模优化方法

Pixelated Source Mask Optimization Based on Multi Chromosome Genetic Algorithm

提出了一种基于多染色体遗传算法(GA)的像素化光源掩模优化(SMO)方法。该方法使用多染色体遗传算法,实现了像素化光源和像素化掩模的联合优化。与采用矩形掩模优化的单染色体GASMO方法相比,多染色体GASMO方法具有更高的优化自由度,可以获得更优的光刻成像质量和更快的优化收敛速度。典型逻辑图形的仿真实验表明,多染色体方法得到的最优光源和最优掩模的适应度值比单染色体方法小7.6%,提高了光刻成像质量。仿真实验还表明,多染色体方法仅需132代进化即可得到适应度值为5200的最优解,比单染色体方法少127代,加快了优化收敛速度。

A pixelated source mask optimization（SMO）method based on multi chromosome genetic algorithm（GA）is introduced.This method uses multi chromosome genetic algorithm to optimize the pixelated source and pixelated mask simultaneously.In comparison with the single chromosome GASMO method that uses rectilinear mask representation,multi chromosome GASMO method can get high imaging quality and fast convergence speed.Simulation results show that the multi chromosome method can get an optimum solution with the fitness value is7.6%,which is smaller than that of the single chromosome method.The multi chromosome method only needs 132 generations to converge to an optimal result with the fitness value of 5200,127 generations less than the single chromosome method,and the optimization convergence speed is accelerated.