18 March 2015 A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction
Author Affiliations +
Abstract
Under the low-k1 lithography process, lithography hotspot detection and elimination in the physical verification phase have become much more important for reducing the process optimization cost and improving manufacturing yield. This paper proposes a highly accurate and low-false-alarm hotspot detection framework. To define an appropriate and simplified layout feature for classification model training, we propose a novel feature space evaluation index. Furthermore, by applying a robust classifier based on the probability distribution function of layout features, our framework can achieve very high accuracy and almost zero false alarm. The experimental results demonstrate the effectiveness of the proposed method in that our detector outperforms other works in the 2012 ICCAD contest in terms of both accuracy and false alarm.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tetsuaki Matsunawa, Jhih-Rong Gao, Bei Yu, David Z. Pan, "A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction", Proc. SPIE 9427, Design-Process-Technology Co-optimization for Manufacturability IX, 94270S (18 March 2015); doi: 10.1117/12.2085790; https://doi.org/10.1117/12.2085790
PROCEEDINGS
11 PAGES


SHARE
Back to Top