Delving into Topology Representation for Layout Pattern: A Novel Contrastive Learning Framework for Hotspot Detection

Recently, machine learning-based techniques have been applied for layout hotspot detection. However, existing methods encounter challenges in capturing the decision boundary across the entire dataset and ignore the geometric properties and topology of the polygons. In this paper, we introduce CLI-HD, a novel contrastive learning framework on layout sequences and images for hotspot detection. Our framework improves the ability to distinguish between hotspots and non-hotspots by similarity computations instead of a single decision boundary. To effectively incorporate geometric information into the model training process, we propose Layout2Seq, which encodes polygon shapes as vectors within sequences that are subsequently fed into the CLIHD. Furthermore, to better represent topology information, we develop an absolute position embedding, replacing the standard position encoders used in Transformer architectures. Extensive evaluations on various benchmarks demonstrate that CLI-HD outperforms current state-of-the-art methods, with an accuracy improvement ranging from $0.82 \%$ to $4.77 \%$ and a reduction in false alarm rates by $\mathbf{4. 9 \%}$ to $\mathbf{2 3. 1 8 \%}$.