Using robust adaptive mixing for statistical fault macromodelling

Abstract

The design and analysis of analogue circuits can be speeded up if accurate macromodels are used in place of full, transistor-level netlists. Similarly, testability analysis of analogue circuits at the transistor level is difficult because of the large CPU times needed for fault simulation. Macromodelling circuits under catastrophic fault conditions is difficult because the faulty behaviour is not easily predicted. Moreover, the variances in faulty behaviour, because of parametric tolerances, are not the same as the variance of the fault free behaviour. An algorithm is presented for statistical fault macromodelling of analogue circuits. The circuit macros are modelled using a robust adaptive mixing algorithm, which is based on mutual information theory and robust statistical methods. The experimental results show that the CPU time required for statistical fault macromodelling is very small and the model accuracy is very high.