Masked Layout Modeling Advances Hotspot Detection

With the rapid advancement of semiconductor technology and the continuous miniaturization of circuit feature sizes, hotspot detection has become an increasingly critical yet challenging task in physical verification workflows. In recent years, numerous deep learning frameworks have been developed to address hotspot detection. However, the performance of these learning-based frameworks is heavily dependent on the quality of the datasets used. However, obtaining a large labeled hotspot dataset with high quality is an extremely time-consuming process. Recently, masked image modeling (MIM) has drawn significant attention for its ability to learn from vast amounts of unlabeled data and has demonstrated effectiveness across a wide range of image tasks. Despite its success, the application of MIM to layout analysis, particularly in the context of semiconductor design, remains largely unexplored. Motivated by the principles of MIM, we propose a transfer learning framework that leverages pretraining through masked layout modeling (MLAM) and subsequently fine-tunes the model on limited labeled hotspot detection datasets. Experimental results on our custom layout datasets demonstrate the effectiveness of our approach.