Logic Circuits Reliability Analysis using Signal Probability and Bayesian Network Concepts

Abstract

Background: Reliability analysis of logic circuits has been widely investigated due to increasing the fault occurrence in modern integrated circuits. Simulation-based and analytical methods are developed to estimate the reliability of logic circuits. Methods: In this paper, a signal probability-based method is presented to estimate the reliability of logic circuits. In the proposed approach, four signal probabilities (correct 0, correct 1, incorrect 0, and incorrect 1) are derived (for every node of the circuit) using a probabilistic transfer matrix (PTM), and the correlation coefficients (CCs) are deployed to resolve the reconvergent fanouts issues. The CCs are defined in a fanout cone and are propagated through the logic gates to the related reconvergent nodes. The Bayesian network concept is applied to achieve more accuracy in the propagation of CCs through the logic gates. Results: The accuracy and scalability of the proposed method are proved by various simulations on benchmark circuits (ISCAS 85, LGSynth91, and ITC99). The proposed method efficiently solves the reconvergent fanout problem. Moreover, the proposed method outperforms the previous methods regarding accuracy and scalability. Conclusion: Simulation results on ISCAS 85, LGSynth91, and ITC99 benchmark circuits show less than 3% average error compared with the accurate simulation-based fault injection method.