LAIDAR: Learning for Accuracy and Ideal Diagnostic Resolution

Abstract

IC diagnosis, as a key-step of yield learning, helps to uncover the root cause of chip failure. High quality diagnosis results, measured in terms of accuracy and resolution, are crucial for physical failure analysis during fast yield ramping. Despite various existing methods for enhancing diagnosis, there is still ample room for further improvement. In this paper, a new machine learning based diagnosis method is proposed for improving both accuracy and resolution. Based on features extracted from tester and simulation data, the goal is to predict whether a defect candidate actually corresponds to the real defect. Specifically, semi-supervised learning is deployed to use unlabeled data to augment model training. In addition, a defect-level learning procedure uses characteristics from similar defects to further improve resolution. Experiments involving virtual and silicon datasets demonstrate significant improvements that include: 6.4× increase in occurrences of perfect diagnosis, and a performance that consistently outperforms other state-of-the-art diagnosis techniques.