Multi-Level Layout Hotspot Detection based on Multi-Classification With Deep Learning

With the development of process technology nodes, hotspot detection has become a critical step in integrated circuit physical design flow. The machine learning-based method has become a competitive candidate for layout hotspot detector with easy training and high speed. Classic methods usually define hotspot detection as a binary classification problem. However, the designer hopes to further divide the hotspot patterns into a series of levels according to their severity to identify and fix these hotspots. In this paper, we designed a multi-classifier based on the convolutional neural network to realize the detection of various levels of hotspot patterns. Unlike classic cross-entropy loss, we proposed a custom loss function to reduce the difference between false predicted levels and corresponding true levels, reducing the adverse effects caused by misclassified samples. Experimental verification results show that our hotspot detector can correctly classify various hotspots levels and has potential advantages for physical designers to fix hotspots.