LLM-HD: Layout Language Model for Hotspot Detection with GDS Semantic Encoding

Yuyang Chen, Yiwen Wu, Jingya Wang, Tao Wu, Xuming He, Jingyi Yu, Hao Geng

Abstract: Layout hotspot detection approaches are challenged by the time-to-market constraint and complex designs under rapid downscaling of technology nodes. Pattern matching and learning-based detectors are proposed as quick detection methods. These layout image-based detectors use images transformed from binary database files of layout like GDSII as their inputs. Italy leads to foreground information (e.g., metal polygons) loss and even distortion when shrinking the image size to fit the approach input. Moreover, plenty of irrelevant background information such as non-polygon pixels is also fed into the model, which hinders the fitting of the model and results in a waste of computational resources. In this work, for the first time, we propose a new layout hotspot detection paradigm, where hotspots are directly detected on binary database files by exploiting a hierarchical GDS semantic representation scheme and a well-designed pre-trained natural language processing (NLP) model. Compared with state-of-the-art (SOTA) works, the proposed detector achieves better results on both the ICCAD2012 metal layer benchmark and the more challenging ICCAD2020 via layer benchmark, which demonstrates the effectiveness and efficiency.