A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis
Igor Aizenberg, Riccardo Belardi, Marco Bindi, Francesco Grasso, S. Manetti, Antonio Luchetta, Maria Cristina Piccirilli
Abstract
In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits.