Location is All You Need: Efficient Lithographic Hotspot Detection Using Only Polygon Locations

With integrated circuits at advanced technology nodes shrinking in feature size, lithographic hotspot detection has become increasingly important. Deep learning, especially convolutional neural networks (CNNs) and graph neural networks (GNNs) have recently succeeded in lithographic hotspot detection, where layout patterns, represented as images or graph features, are classified into hotspots and non-hotspots. However, with increasingly sophisticated CNN architectural designs, CNN-based hotspot detection requires excessive training and inference costs with expanding model sizes but only marginally improves detection accuracy. Existing GNN-based hotspot detector requires more intuitive and efficient layout graph feature representation. Driven by the understanding that lithographic hotspots result from complex interactions among metal polygons through the light system, we propose the absolute and relative locations of metal polygons are all we need to detect hotspots of a layout clip. We propose a novel layout graph feature representation for hotspot detection where the coordinates of each polygon and the distances between them are taken as node and edge features, respectively. We design an advanced GNN architecture using graph attention and different feature update functions for different edge types of polygons. Our experimental results demonstrate that our GNN hotspot detector achieves the highest hotspot accuracy and the lowest false alarm on different datasets. Notably, we employ one-third of the graph features of the previous GNN hotspot detector and achieve higher accuracy. We outperform all CNN hotspot detectors with higher accuracy, up to 32× speed up in inference time, and 64× reduction in model size.