Hotspot detection in large-scale layout with proposal sampling and feature parameters optimization

Hotspot detection focused on lithography induced defects becomes crucial at advanced node due to the increasing complexity of the design and manufacture process. Compared with traditional lithography simulation techniques for hotspot detection, machine-learning-based methods have shown significant advantages attributing to the efficiency and generality of their model. However, most convolutional neural network-based hotspot detector can only inference a layout pattern at once. Therefore, sampling clip patterns from the detected layout is the bottleneck of the whole process and determines the performance of hotspot detection. We designed a flow to generate filter rules by clustering analysis of known hotspots, which can efficiently extract layout clips as detected samples to hotspot classifier. We further propose a feature parametric optimization method to extract valuable graphic features for classifiers and reduce redundancy from context patterns. Experimental results demonstrate that these techniques improve the accuracy of hotspots detection.