Simulation and Fault Diagnosis in Post-Manufacturing Mixed Signal Circuits

Abstract

A major problem in circuit board remanufacturing is the identification of parametric faults from age or stress to the individual passive components. We propose a deep machine learning system for simulating and identifying such faults. A simulated dataset is generated for the most common faults in a circuit. This dataset is used to train deep machine learning classification algorithms to identify and classify the faults. The accuracy of system is measured by comparing with real circuit boards in operation.