光学接近校正
可制造性设计
计算机科学
平版印刷术
加速
像素
升级
软件
核(代数)
忠诚
GSM演进的增强数据速率
光刻
计算机工程
人工智能
算法
并行计算
工程类
光学
过程(计算)
材料科学
数学
纳米技术
物理
组合数学
操作系统
程序设计语言
机械工程
电信
作者
Xu Ma,Shangliang Jiang,Jie Wang,Bingliang Wu,Zhiyang Song,Yanqiu Li
标识
DOI:10.1016/j.mee.2016.10.006
摘要
Pixel-based optical proximity correction (PBOPC) is currently a key resolution enhancement technique to push the resolution limit of optical lithography. However, the increasing scale, density and complexity of modern integrated circuits pose new challenges to both of the OPC computational intensity and mask manufacturability. This paper aims at developing a practical OPC algorithm based on a machine learning technique to effectively reduce the PBOPC runtime and mask complexity. We first divide the target layout into small regions around corners and edge fragments. Using a nonparametric kernel regression technique, these small regions are then filled in by the weighted linear combination of a subset of training OPC examples selected from the pre-calculated libraries. To keep balance between the image fidelity and mask complexity, we use an edge-based OPC (EBOPC) library to synthesize the OPC patterns in non-critical areas, while use another PBOPC library for hotspots. In addition, a post-processing method is developed to refine the regressed OPC pattern so as to guarantee the final image fidelity and mask manufacturability. Experimental results show that, compared to a currently professional PBOPC software, the proposed algorithm can achieve approximately two-fold speedup and more manufacture-friendly OPC patterns.
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