Fault macromodeling for analog/mixed-signal circuits

Abstract

In this paper we propose an efficient fault macromodeling technique for analog/mixed-signal circuits. We formulate the fault macromodeling problem as a problem of deriving the macro parameter set B based on the performance parameter set P of the transistor-level faulty circuit. The fault macromodel is intended to be used for efficient macro-level fault simulation. In such applications, a common approach to speeding up the macromodeling process is to generate a large number of data pairs (P, B) (the training set) and interpolate an empirical mapping function B=F(P) based on the training set. In our technique, generation of each data pair requires only one run of macro-level simulation, as opposed to multiple runs of macro-level simulation required by iterative fault macromodeling techniques. We also propose a cross-correlation-based technique to select a subset of parameters from the high dimensional parameter set P to speed up function interpolation. We demonstrate the effectiveness and efficiency of our proposed fault macromodeling technique by showing some preliminary, experimental results on an industrial design.