arrow
返回

Gradient-Based Source and Mask Optimization in Optical Lithography

delete2011-10-01
delete66
PRE
AI
P
Peng Yao *
J
Jinyu Zhang
Y
Yan Wang
Z
Zhiping Yu
DOI:10.1109/TIP.2011.2131668delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
Source and mask optimization (SMO) has been proposed recently as an effective solution to extend the lifespan of conventional 193 nm lithography, although the process is computationally intensive. In this study, we propose a highly effective and efficient method for source optimization and improve a previous method for mask optimization. An SMO framework is implemented by integrating them. Based on pixel-based source and mask representation, the gradients of the objective function are utilized to guide optimization. In addition to maintain the image fidelity, extra penalties are added into the objective function to increase the depth of focus (DOF) and regularize the source and mask patterns. In our SMO framework, a specially designed mask optimization procedure is performed to enhance the algorithm robustness. Afterward, the source optimization and mask optimization are performed alternatively. Convergence results can be acquired using only two or three iteration cycles. This method is demonstrated using two mask patterns with critical dimensions of 45 nm, including a periodic array of contact holes and a cross gate design. The results show that our method can provide great improvements in both image quality and DOF. The robustness of our method is also verified using different initial conditions.
Keyword:
Depth of focus (DOF)
image fidelity
imaging model
inverse lithography techniques (ILT)
optical lithography
resolution enhancement techniques (RET)
source and mask optimization (SMO)
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

IEEE Transactions on Image Processing 封面图
IEEE Transactions on Image Processing
IF:
13.7
论文数:
1.0W
被引数:
8.4W

机构

T
tsinghua university
学者数:
11.9W
论文数: 10.0W
被引数: 137
引用论文

引用论文

err分享
err收藏
err分享
err收藏
err分享
err收藏
学者 查看更多内容