Fault diagnosis of analog circuits based on machine learning

We discuss a fault diagnosis scheme for analog integrated circuits. Our approach is based on an assemblage of learning machines that are trained beforehand to guide us through diagnosis decisions. The central learning machine is a defect filter that distinguishes failing devices due to gross defects (hard faults) from failing devices due to excessive parametric deviations (soft faults). Thus, the defect filter is key in developing a unified hard/soft fault diagnosis approach. Two types of diagnosis can be carried out according to the decision of the defect filter: hard faults are diagnosed using a multi-class classifier, whereas soft faults are diagnosed using inverse regression functions. We show how this approach can be used to single out diagnostic scenarios in an RF low noise amplifier (LNA).