Academic Journal
Enhanced Lithographic Hotspot Detection via Multi-Task Deep Learning With Synthetic Pattern Generation
| العنوان: | Enhanced Lithographic Hotspot Detection via Multi-Task Deep Learning With Synthetic Pattern Generation |
|---|---|
| المؤلفون: | Xinguang Zhang, Shiyang Chen, Zhouhang Shao, Yongjie Niu, Li Fan |
| المصدر: | IEEE Open Journal of the Computer Society, Vol 6, Pp 141-152 (2025) |
| بيانات النشر: | IEEE, 2025. |
| سنة النشر: | 2025 |
| المجموعة: | LCC:Electronic computers. Computer science LCC:Information technology |
| مصطلحات موضوعية: | Integrated circuit, lithographic hotspot detection, multi-task learning, synthetic pattern generation, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2644-1268 |
| Relation: | https://ieeexplore.ieee.org/document/10772617/; https://doaj.org/toc/2644-1268 |
| DOI: | 10.1109/OJCS.2024.3510555 |
| URL الوصول: | https://doaj.org/article/4a1eaeca987746da994a5394aa9b42fb |
| رقم الانضمام: | edsdoj.4a1eaeca987746da994a5394aa9b42fb |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 26441268 |
|---|---|
| DOI: | 10.1109/OJCS.2024.3510555 |