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.