Novel Feature Vectors Considering Distances between Wires for Lithography Hotspot Detection

In this paper, we propose some feature vectors that better represent characteristics of lithography hotspots for machine learning based hotspot detection. In the lithography process, which is one of the LSI fabrication processes, a local layout pattern with a high failure probability is called a hotspot. It is desirable to find and remove such hotspots before starting the fabrication processes, since the reproduction of photomasks for LSI fabrication takes a huge cost. Our feature vectors consider distances between wires, which have a great effect on accuracies of images developed on a wafer, to efficiently find hotspots. Experimental results showed that our proposed feature vectors achieved lower undetected error probabilities compared to some existing ones including a well-known one.