Accurate Estimation of Circuit Delay Variance with Limited Monte Carlo Simulations Using Bayesian Inference

An accurate prediction of circuit delay distribution is essential to verify the timing closure of digital circuits and to estimate the parametric yield. In the presence of process variations, the most accurate technique for circuit delay prediction is using Monte Carlo simulations. However, to get an accurate estimate, a large number of Monte Carlo simulations are required which is infeasible in the case of large circuits. Although statistical timing analysis techniques are widely used to predict the circuit delay distribution in a reasonable run time, the standard deviation of circuit delay is often inaccurate. In this work, an efficient technique for circuit delay prediction using limited number of Monte Carlo simulations is proposed. This is done using the concept of Bayesian inference with the results from statistical timing analysis as the prior information. The results indicate that combining statistical timing analysis along with limited number of Monte Carlo simulations increases the accuracy of prediction of circuit delay variance. The number of Monte Carlo simulations can be decided based on the accuracy requirements or run time constraints.