A technique of analog circuits testing and diagnosis based on neuromorphic classifier

Abstract

The technique of functional testing the analog integrated circuits based on neuromorphic classifier (NC) has been proposed. The structure of NC providing detection both catastrophic and parametric faults taking into account the tolerance on parameters of internal components has been described. The NC ensures the associative fault detection reducing a time on diagnosis in comparison with parametric tables. The approach to selection of essential characteristics used for the NC training has been represented. The wavelet transform of transient responses, Monte Carlo method and statistical processing are used for the essential characteristics selection with maximum distance between faulty and fault-free conditions. The experimental results for the active filter demonstrating high fault coverage and low likelihood of alpha and beta errors at diagnosis have been shown.