Enhancements of Model and Method in Lithography Hotspot Identification

The manufacturing of integrated circuits (ICs) has been continuously improved through the advancement of fabrication technology nodes. However the lithography hotspots (HSs) caused by optical diffraction problems seriously affect the yield and reliability of ICs. Although lithography simulation can accurately capture the HSs through physically simulating the lithography process, it requires a lot of computing resources, which usually takes > 100 CPU ·
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[1]. Due to the image recognition nature, the state-of-the-art HS identification algorithms based on deep learning have obvious advantages in reducing run time comparing to the traditional algorithms. However, its accuracy still needs to be enhanced since there are many false alarms of non-hotspots (NHSs) and escapes of the real HSs, which makes it difficult to be a signoff technique. In this paper, we propose two enhancements in HS identification. First, a hybrid deep learning model is proposed in lithography HS identification, which includes a CNN model to combine physical features. Second, an ensemble learning method is proposed based on multiple submodels. The proposed enhanced model and method can achieve high HS identification accuracy on the benchmarks 1–4 of the ICCAD 2012 dataset with recall> 98.8%. In addition, it can achieve even 100% recall on the benchmark 1 and benchmark 3 while maintaining the precision at a high level with 53.6% and 87.1%, respectively. Moreover, for the first time it can achieve not only 100% recall on benchmark 5, but also high precision of 61.8%, which is much higher than any published deep learning methods for HSs identification, as far as we know. The proposed model and methodology can be applied in industrial IC designs due to its effectiveness and efficiency.