Data learning based diagnosis

Traditional diagnosis of defects is based on an assumed fault model. A failing chip is diagnosed to find the subset of faults that can best explain the failure. This paper illustrates a link between this traditional perspective of diagnosis and a new perspective where diagnosis is seen as a form of data learning. We explain that both defect diagnosis and data learning are solving so-called ill-posed problems and the technique for solving such a problem is called regularization. We illustrate a diagnosis framework that employs various data learning techniques to implement two diagnosis approaches: feature ranking and rule extraction. This diagnosis framework is designed to uncover design-related issues that cause systematic uncertainties or any unexpected behavior in silicon. We review the work that has been accomplished for implementing this framework and further discuss issues with its practical application.