Fault Classification in Mixed Signal Circuits using Artificial Neural Networks

Abstract

Objectives: The manufactured Integrated circuits are usable only when they are free from any types of faults or error. This is even more essential in complex circuits like analog and mixed signal circuits. This paper focuses on the fault classification in the analog portion of mixed signal circuits using artificial neural networks. Methods/Statistical Analysis: All possible catastrophic possible faults in mixed signal circuits are introduced and simulated in an exhaustive manner using Cadence Simulation package. Faults are introduced one at a time and parameters were recorded. The parametric variations obtained through simulation were normalized and are used to suitably train artificial neural networks by creating them as database. The artificial neural network is trained such that it can identify correct functioning of the circuit from its faulty operation and also to further distinguish it into the component at fault, along with the type of catastrophic or hard fault. Findings: Comparative results of Feed forward neural networks trained with Levenberg-Marquardt algorithm and Radial basis function networks in the classification of faults are provided. Application/Improvements: This can be extended for the other mixed signal circuits by creating the training data using the software simulations. The hardware implementation is possible using the embedded controllers and other types of ANN can also be selected if necessary.