中文题目: 基于多染色体遗传算法的像素化光源掩模优化方法
外文题目: Pixelated Source Mask Optimization Based on Multi Chromosome Genetic Algorithm
作者: 杨朝兴; 李思坤; 王向朝
刊名: 光学学报
年: 2016 卷: 36 期: 8 页： 811001
中文关键词:
成像系统; 光学制造; 光刻; 光源掩模优化; 分辨率增强技术; 遗传算法; 多染色体
英文关键词:

imaging systems; optical fabrication; optical lithography; source mask optimization; resolution enhancement technology; genetic algorithm; multi chromosome
中文摘要:
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

英文摘要:
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 is 7.6%,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 5200,127generations less than the single chromosome method,and the optimization convergence speed is accelerated.


文献类型: 期刊论文
正文语种: Chinese
收录类别: CSCDEI
DOI: 10.3788/AOS201636.0811001


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