Machine Learning based Lithographic Hotspot Detection with Critical-Feature Extraction and Classification

被引：65

作者：

Ding, Duo ^{[1
]}

Wu, Xiang ^{[1
]}

Ghosh, Joydeep ^{[1
]}

Pan, David Z. ^{[1
]}

机构：

[1] Univ Texas Austin, ECE Dept, Austin, TX 78712 USA

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON INTEGRATED CIRCUIT DESIGN AND TECHNOLOGY, PROCEEDINGS | 2009年

关键词：

D O I：

10.1109/ICICDT.2009.5166300

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we present a fast and accurate lithographic hotspot detection flow with a novel MLK (Machine Learning Kernel), based on critical feature extraction and classification. In our flow, layout binary image patterns are decomposed/analyzed and critical lithographic hotspot related features are defined and employed for low noise MLK supervised training. Combining novel critical feature extraction and MLK supervised training procedure, our proposed hotspot detection flow achieves over 90% detection accuracy on average and much smaller false alarms (10% of actual hotspots) compared with some previous work [9,13], without CPU time overhead.

引用

页码：219 / 222

页数：4