Lithography Hotspot Detection with FFT-based Feature Extraction and Imbalanced Learning Rate

Abstract

With the increasing gap between transistor feature size and lithography manufacturing capability, the detection of lithography hotspots becomes a key stage of physical verification flow to enhance manufacturing yield. Although machine learning approaches are distinguished for their high detection efficiency, they still suffer from problems such as large-scale layout and class imbalance. In this article, we develop a hotspot detection model based on machine learning with high performance. In the proposed model, we first apply an Fast Fourier Transform--based feature extraction method that can compress large-scale layout to a multi-dimensional representation with much smaller size while preserving the discriminative layout pattern information to improve the detection efficiency. Second, addressing the class imbalance problem, we propose a new technique called imbalanced learning rate and embed it into the convolutional neural network model to further reduce false alarms without accuracy decay. Compared with the results of current state-of-the-art approaches on ICCAD 2012 Contest benchmarks, our proposed model can achieve better solutions in many evaluation metrics, including the official metrics.