Semisupervised Lithography Hotspot Detection Model Based on Dual-Branch Auxiliary Classification

Abstract

Due to the low accuracy and high false alarm rate of conventional semisupervised lithography hotspot detection models, we propose a semisupervised hotspot detection model based on dual-branch auxiliary classification comprising a classification stream, a dual-branch auxiliary classification stream, and a clustering stream. The classification stream assigns labels to input samples. The auxiliary classification stream consisting of two branches validates the classification results. Moreover, the clustering stream estimates the confidence of the sample labels. Due to the imbalance of the dataset, the model integrates a random data augmentation method to increase the hotspot samples and thus enhance model performance. Additionally, false positive rate (FPR) is used to assess model performance across all benchmarks in the ICCAD 2012 dataset. The experimental results demonstrate that our model achieves higher accuracy and a lower FPR while requiring less overall detection and simulation time across different proportions of labeled samples compared with the state-of-the-art model.