Automated, accurate macromodelling of digital aggressors for power/ground/substrate noise prediction

Noise analysis and power distribution network reliability assessment is extremely important in deep sub-micron digital and mixed-signal circuit design. Both relate closely to the nonlinear loading impact of digital circuits. Consequently, accurate estimation of the latter is critical. In this paper, we present extraction techniques that automatically generate a family of small, time-varying macromodels for digital cell libraries, at the time of their library characterization. Our approach is based on importing and adapting the time-varying pade (TVP) method, for linear time-varying (LTV) model reduction, from the mixed-signal macromodelling domain. Our approach features naturally higher accuracy than previous ones, and in addition, offers the user a tradeoff between accuracy and macromodel complexity. A key attraction of our approach is that it can be merged into cell library extraction methodologies to produce accurate-by-construction noise models for digital blocks. Simulations and comparisons confirming the efficacy of our approach are provided.