On Improving Hotspot Detection Through Synthetic Pattern-Based Database Enhancement

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 3x reduction in classification error in comparison to the state-of-the-art.