HyDAS: Hybrid Domain Deformed Attention for Selective Hotspot Detection

Technology node scaling is challenged in many aspects, including pitch reduction, patterning flexibility, and lithography process variability during manufacturing. Without exception, layout hotspot detection, one of the critical steps to achieving design closure, also requires upgrading the associated techniques. With the rapid development of deep learning techniques, the detector exploiting convolutional neural network (CNN) is superior to ones based on pattern matching and classical machine learning algorithms. However, due to the local nature of CNN, the traditional CNN-based detector fails to model the relationship between the patterns in a large-sized layout, resulting in ignoring the impact of light propagation and some optical effects during photolithography. Even worse, another challenge arises from the fact that engineers cannot fully trust the results of learning model-based detectors, especially when handling some complicated layout patterns in practice. This makes it very difficult to deploy the detectors. Observing the facts, we propose a vision transformer (ViT) model-based layout hotspot detector with a deformed attention mechanism, where the training paradigm is inspired by the large pre-trained foundation model (e.g., OpenAI’s GPT-n series) and fine-tuning. Considering the light diffraction during photolithography, the hybrid domain (i.e., spatial and spectral domain) layout inputs via multi-channel are leveraged. Besides, our proposed detector integrates a selective option where the model can choose to do prediction or send to engineers based on the misclassification risk level. Experimental results on the ICCAD2012 metal layer benchmarks and ICCAD2020 via layer benchmarks demonstrate the effectiveness and efficiency of our approach. We have made the ICCAD2020 dataset publicly available to support further research in hotspot detection, enable benchmarking across different process nodes and layout types, and facilitate reproducibility in the field. The dataset is accessible at https: //github.com/shadowior/ICCAD2020.