A General Layout Pattern Clustering Using Geometric Matching-based Clip Relocation and Lower-bound Aided Optimization

Author:

He Xu1ORCID,Wang Yao2ORCID,Fu Zhiyong1ORCID,Wang Yipei1ORCID,Guo Yang2ORCID

Affiliation:

1. Hunan University, China

2. National University of Defense Technology, China

Abstract

With the continuous shrinking of feature size, detection of lithography hotspots has been raised as one of the major concerns in Design-for-Manufacturability (DFM) of semiconductor processing. Hotspot detection, along with other DFM measures, trades off turnaround time for the yield of IC manufacturing, and thus a simplified but wide-ranging pattern definition is a key to the problem. Layout pattern clustering methods, which group geometrically similar layout clips into clusters, have been vastly proposed to identify layout patterns efficiently. To minimize the clustering number for subsequent DFM processing, in this article, we propose a geometric-matching-based clip relocation technique to increase the opportunity of pattern clustering. Particularly, we formulate the lower bound of the clustering number as a maximum-clique problem, and we have also proved that the clustering problem can be solved by the result of the maximum-clique very efficiently. Compared with the experimental results of the state-of-the-art approaches on ICCAD 2016 Contest benchmarks, the proposed method can achieve the optimal solutions for all benchmarks with very competitive runtime. To evaluate the scalability, the ICCAD 2016 Contest benchmarks are extended and evaluated. Moreover, experimental results on the extended benchmarks demonstrate that our method can reduce the cluster number by 16.59% on average, while the runtime is 74.11% faster on large-scale benchmarks compared with previous works.

Funder

National Natural Science Foundation of China

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Reference26 articles.

1. Wei-Chun Changet al. 2017. iClaire: A fast and general layout pattern classification algorithm. In Proceedings of the 54th Annual Design Automation Conference (DAC’17). 1–6.

2. iClaire: A Fast and General Layout Pattern Classification Algorithm With Clip Shifting and Centroid Recreation

3. Kuan-Jung Chen, Yu-Kai Chuang, Bo-Yi Yu, and Shao-Yun Fang. 2017. Minimizing cluster number with clip shifting in hotspot pattern classification. In Proceedings of the 54th Annual Design Automation Conference (DAC’17). 1–6.

4. Improved Tangent Space-Based Distance Metric for Lithographic Hotspot Classification

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3