Layout Hotspot Detection With Feature Tensor Generation and Deep Biased Learning

Abstract

Detecting layout hotspots is a key step in the 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 inevitably, performance degradation occurs. To overcome these issues, in this paper, we develop a deep learning framework for high performance and large scale hotspot detection. First, we use feature tensor generation 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 (BL) algorithm to train the convolutional neural network to further improve detection accuracy with small false alarm penalties. In addition, to simplify the training procedure and seek a better tradeoff between accuracy and false alarms, we extend the original BL to a batch BL algorithm. Experimental results show that our framework outperforms previous machine learning-based hotspot detectors in both ICCAD 2012 Contest benchmarks and large scale industrial benchmarks. Source code and trained models are available at https://github.com/phdyang007/dlhsd.