Machine learning based lithographic hotspot detection with critical-feature extraction and classification

• Published in: 2009 IEEE International Conference on IC Design and Technology pp. 219 - 222
• Main Authors: Duo Ding, Xiang Wu, Ghosh, J., Pan, D.Z.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2009
• Subjects: Algorithm design and analysis; Artificial neural networks; Data mining; Fabrication; Feature extraction; Kernel; Machine learning; Manufacturing processes; Semiconductor device manufacture; Semiconductor device noise
• ISBN: 1424429331; 9781424429332
• ISSN: 2381-3555
• DOI: 10.1109/ICICDT.2009.5166300

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.