Layout hotspot detection with feature tensor generation and deep biased learning

Detecting layout hotspots is one of the key problems in physical verification flow. Although machine learning solutions show benefits over lithography simulation and pattern matching based methods, it is still hard to select a proper model for large scale problems and it is inevitable that performance degradation will occur. To overcome these issues, in this paper we develop a deep learning framework for high performance and large scale hotspot detection. First, feature tensor generation is proposed to extract representative layout features that fit well with convolutional neural networks while keeping the spatial relationship of the original layout pattern with minimal information loss. Second, we propose a biased learning algorithm to train the convolutional neural network to further improve detection accuracy with small false alarm penalties. Experimental results show that our framework outperforms previous machine learning-based hotspot detectors in both the ICCAD 2012 Contest benchmarks and large scale industrial benchmarks.

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