2024/09/16 by Hao-Chiang Shao, Shao, Hao-Chiang, Guan-Yu Chen +11
Computer Science · Engineering · #Advancements in Photolithography Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Integrated Circuits and Semiconductor Failure Analysis #VLSI and Analog Circuit Testing
paper · pdf · doi:10.48550/arxiv.2409.10021
openalex publication_date 2024/09/16 · openalex created_date 2024/10/24 · openalex updated_date 2026/07/28
Recent advances in VLSI fabrication technology have led to die shrinkage and increased layout density, creating an urgent demand for advanced hotspot detection techniques. However, by taking an object detection network as the backbone, recent learning-based hotspot detectors learn to recognize only the problematic layout patterns in the training data. This fact makes these hotspot detectors difficult to generalize to real-world scenarios. We propose a novel lithography simulator-powered hotspot detection framework to overcome this difficulty. Our framework integrates a lithography simulator with an object detection backbone, merging the extracted latent features from both the simulator and the object detector via well-designed cross-attention blocks. Consequently, the proposed framework can be used to detect potential hotspot regions based on I) the variation of possible circuit shape deformation estimated by the lithography simulator, and ii) the problematic layout patterns already known. To this end, we utilize RetinaNet with a feature pyramid network as the object detection backbone and leverage LithoNet as the lithography simulator. Extensive experiments demonstrate that our proposed simulator-guided hotspot detection framework outperforms previous state-of-the-art methods on real-world data.