中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Pixelated Source Mask Optimization Based on Multi Chromosome Genetic Algorithm

文献类型:期刊论文

作者Yang CX(杨朝兴); Li SK(李思坤); Wang XC(王向朝)
刊名光学学报
出版日期2016
卷号36期号:8页码:811001
通讯作者yangcoloy@siom.ac.cn ; wxz26267@siom.ac.cn
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.
收录类别CSCD
资助信息国家自然科学基金
CSCD记录号CSCD:5777797
WOS记录号CSCD:5777797
源URL[http://ir.siom.ac.cn/handle/181231/28517]  
专题上海光学精密机械研究所_信息光学与光电技术实验室
作者单位中国科学院上海光学精密机械研究所
推荐引用方式
GB/T 7714
Yang CX,Li SK,Wang XC. Pixelated Source Mask Optimization Based on Multi Chromosome Genetic Algorithm[J]. 光学学报,2016,36(8):811001.
APA 杨朝兴,李思坤,&王向朝.(2016).Pixelated Source Mask Optimization Based on Multi Chromosome Genetic Algorithm.光学学报,36(8),811001.
MLA 杨朝兴,et al."Pixelated Source Mask Optimization Based on Multi Chromosome Genetic Algorithm".光学学报 36.8(2016):811001.

入库方式: OAI收割

来源:上海光学精密机械研究所

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。