Layout hotspot detection is one of the most important issues for the reliability enhancement of integrated circuits. Machine learning-based hotspot detectors have shown their advantages of efficiency and generalization compared with computationally intensive lithography process simulation. However, most machine learning-based hotspot detectors only accept layout clips of fixed size as input with the potential defect whose location is restricted at the center of each clip. Therefore, they cannot be used directly for multiple hotspots detection in a large area, which occurs frequently in real design cases. In this article, we build a new end-to-end hotspot detector based on a fully convolutional network, which has the flexibility of detecting a various number of hotspots in a layout of any size at one time. Moreover, we also develop a transfer learning scheme matching our proposed detector network, which can reduce the requirement of sample number when setting up a new model for a more advanced technology node. The experimental results demonstrate our proposed hotspot detector outstanding among state-of-the-art works and the transfer learning scheme is effective.

Lithography simulation is computationally expensive for hotspot detection. Machine learning-based hotspot detection is a promising technique to reduce the simulation overhead. However, most learning approaches rely on a large amount of training data to achieve good accuracy and generality. At the early stage of developing a new technology node, the amount of data with labeled hotspots or nonhotspots is very limited. In this paper, we propose a semisupervised hotspot detection with self-paced multitask learning paradigm, leveraging both data samples with/without labels to improve model accuracy and generality. Experimental results demonstrate that our approach can achieve 4.6%-6.5% better accuracy at the same false alarm levels than the state-of-the-art work using 10%-50% of training data.

Lithography simulation is computationally expensive for hotspot detection. Machine learning based hotspot detection is a promising technique to reduce the simulation overhead. However, most learning approaches rely on a large amount of training data to achieve good accuracy and generality. At the early stage of developing a new technology node, the amount of data with labeled hotspots or non-hotspots is very limited. In this paper, we propose a semi-supervised hotspot detection with self-paced multi-task learning paradigm, leveraging both data samples w./w.o. labels to improve model accuracy and generality. Experimental results demonstrate that our approach can achieve 2.9--4.5% better accuracy at the same false alarm levels than the state-of-the-art work using 10%-50% of training data. The source code and trained models are released on https://github.com/qwepi/SSL.