Modelling the unmodellable: algorithmic fault diagnosis

In its most basic form, algorithmic fault diagnosis consists of using a fault model to predict the behavior of faulty circuits, comparing these predictions to the actual observed behavior of defective chips, and identifying the predicted behavior(s) which most closely match the observations. The goal of the process is to enable failure analysis by identifying promising locations for further study. The process is successful if the actual defect is contained in the list of possible locations, and if that list is sufficiently small to permit a failure analysis engineer to investigate them. In short, advances continue in both fault models and matching algorithms, which together continue to improve the effectiveness of algorithmic fault diagnosis, in spite of the fact that duplicating the exact behavior of defects remains elusive