On Improving Hotspot Detection Through Synthetic Pattern-Based Database Enhancement

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems 2021-12, Vol.40 (12), p.2522-2527
• Main Authors: Reddy, Gaurav Rajavendra, Xanthopoulos, Constantinos, Makris, Yiorgos
• Format: Article
• Language: English
• Subjects: Database enhancement; Design defects; design for manufacturability; Design for manufacture; Design of experiments; False alarms; Feature extraction; Image edge detection; Layout; lithographic hotspot detection; Lithography; Machine learning; Pattern formation; Pattern generation; Pattern matching; physical design; synthetic pattern generation; Transforms
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2021.3049285

Design hotspots are layout patterns which may cause defects due to complex design and process interactions. Several machine learning and pattern matching-based methods have been proposed to identify and correct them early during design stages. However, almost all of them suffer from high false-alarm rates, mainly because they are oblivious to the root causes of hotspots. In this work, we seek to address this limitation by using a novel database enhancement approach through synthetic pattern generation based on a carefully crafted design of experiments. We evaluate the effectiveness of the proposed method using industry-standard tools and designs and demonstrate more than 3\times reduction in classification error in comparison to the state-of-the-art.