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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 light source mask optimization (SMO) method based on multi-chromosome genetic algorithm (GA) is proposed. The method uses a polychrome genetic algorithm to achieve a joint optimization of a pixelated light source and a pixilated mask. Compared with the single-chromosome GASMO method using rectangular mask optimization, the multi-chromosome GASMO method has a higher optimization degree of freedom, which can achieve better lithography imaging quality and faster optimization convergence rate. Simulation results show that the fitness of optimal light source and optimal mask obtained by multi-chromosome method is 7.6% smaller than that of single-chromosome method, which improves the lithography imaging quality. The simulation results also show that the multi-chromosome method can obtain the optimal solution with fitness value of 5200 only through evolution of 132 generations, which is 127 generations less than the single chromosome method, which accelerates the optimization convergence speed.