A Hybrid Approach to Nonlinear Macromodel Generation for Time-Varying Analog Circuits
Modeling frequency-dependent nonlinear characteristics of complex analog blocks and subsystems is critical for enabling efficient verification of mixed-signal system designs. Recent progress has been made for constructing such macromodels, however, their accuracy and/or efficiency can break down for certain problems, particularly those with high-Q filtering. In this paper we explore a novel hybrid approach for generating accurate analog macromodels for time-varying weakly nonlinear circuits. The combined benefits of nonlinear Padé approximations and pruning by exploitation of the system's internal structure allows us to construct nonlinear circuit models that are accurate for wide input frequency ranges, and thereby capable of modeling systems with sharp frequency selectivity. Such components are widely encountered in analog signal processing and RF applications. The efficacy of the proposed approach is demonstrated by the modeling of large time-varying nonlinear circuits that are commonly found in these application areas.