PointDetector: Large-Scale Lithography Hotspot Detection via Point Cloud Classification

Lithography hotspot detection is essential for ensuring yield and reliability in integrated circuit manufacturing. However, existing approaches typically analyze small layout clips in isolation, resulting in inefficient and fragmented detection workflows. In this work, we propose a novel formulation of hotspot detection as a point cloud classification problem and introduce a tailored neural network architecture based on submanifold sparse convolution to efficiently process large layouts. Our method operates directly on polygon segments used in mask optimization, enabling fine-grained predictions aligned with downstream hotspot fixing. By representing layouts as point clouds and leveraging the sparsity of the data, our approach supports large-scale detection with a single forward pass, improving runtime over the compared clip-based methods. Experimental results on large benchmark datasets show that our method achieves up to 20 × faster inference than existing detection-based techniques while also improving precision, recall, F1 score, and false alarms by 30.0%, 59.6%, 46.0%, and 80.8%, respectively, on our large-scale benchmark. These results highlight the potential of our approach to enable fast, scalable, and fine-grained hotspot detection for advanced IC manufacturing.