A Fuzzy-Matching Model With Grid Reduction for Lithography Hotspot Detection

In advanced IC manufacturing, as the gap increases between lithography optical wavelength and feature size, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which extracts appropriate feature vectors of hotspot and nonhotspot patterns. Our model can dynamically tune appropriate fuzzy regions around known hotspots. Based on this paper, we develop a fast algorithm for lithography hotspot detection with high accuracy of detection and low probability of false-alarm counts. In addition, since higher dimensional size of feature vectors can produce better accuracy but requires longer run time, this paper proposes a grid reduction technique to significantly reduce the CPU run time with very minor impact on the advantages of higher dimensional space. Our results are very encouraging, with average 94.5% accuracy and low false-alarm counts on a set of test benchmarks.