An imbalance aware lithography hotspot detection method based on HDAM and pre-trained GoogLeNet

Abstract

Due to the continuous shrinkage of transistor size and the ever-increasing complexity of integrated circuit design layout, great challenges arise in optical lithography—any defect on the mask will be transferred to the silicon wafer, which may lead to severe defects such as open circuit and short circuit. These defects on masks are called hotspots. Before transferring the circuit layout on the mask to the silicon wafer, the entire mask must be inspected to accurately find out the hotspots before optical lithography. Although traditional lithography hotspot detection approaches, such as pattern matching and machine learning, have gained satisfactory results the performance of the model degrades when encountering problems such as complex layout and data imbalance. In this paper, a hotspot detection method based on hybrid data enhancement, data compression and pre-trained GoogLeNet is proposed to solve the aforementioned problems. Our study shows that the average recall rate can be up to 98.3%. Meanwhile, the false alarm is reduced and the F1-score is 63.5%. Experimental results show that the proposed method achieves better performance on the ICCAD 2012 contest benchmark compared to hotspot detection methods based on deep or representative machine learning.