Enhanced Lithographic Hotspot Detection via Multi-Task Deep Learning With Synthetic Pattern Generation

Abstract

Lithographic hotspot detection is crucial for ensuring manufacturability and yield in advanced integrated circuit (IC) designs. While machine learning approaches have shown promise, they often struggle with detecting truly-never-seen-before (TNSB) hotspots and reducing false alarms on hard-to-classify (HTC) patterns. This article presents a novel multi-task deep learning framework for lithographic hotspot detection that addresses these challenges. Our key contributions include: (1) A synthetic pattern generation method based on early design space exploration (EDSE) to augment training data and improve TNSB hotspot detection; (2) A multi-task convolutional neural network architecture that jointly performs hotspot classification and localization; and (3) An adaptive loss function that balances hotspot detection accuracy and false alarm reduction. Experimental results on the ICCAD-2019 benchmark dataset demonstrate that our approach achieves 98.5% accuracy in hotspot detection with only 1.2% false alarm rate, significantly outperforming state-of-the-art methods. Furthermore, we show a 22% improvement in TNSB hotspot detection and a 5X reduction in false alarms on HTC patterns compared to previous techniques. The proposed framework provides a robust solution for lithographic hotspot detection in early stages of IC design, enabling more efficient design-for-manufacturability optimization.