Tuning the accuracy of rational macromodels to nominal load conditions

We address the generation of broadband macromodels of complex linear systems via rational curve fitting. We show that standard approaches may not ensure that the macromodel accuracy is preserved in system-level simulations, under loading conditions that are different from the adopted identification settings. Our main contribution is an automated procedure for the definition of a frequency-dependent norm weighting strategy that tunes the macromodel accuracy for a specific nominal termination network, thus improving model robustness under realistic operation.