Reduced-order modeling of time-varying systems

We present algorithms for reducing large circuits, described at SPICE-level detail, to much smaller ones with similar input-output behavior. A key feature of our method, called time-varying Pade (TVP), is that it is capable of reducing time-varying linear systems. This enables it to capture frequency-translation and sampling behavior, important in communication subsystems such as mixers and switched-capacitor filters, Krylov-subspace methods are employed in the model reduction process. The macromodels can be generated in SPICE-like or AHDL format, and can be used in both time- and frequency-domain verification tools. We present applications to wireless subsystems, obtaining size reductions and evaluation speedups of orders of magnitude with insignificant loss of accuracy. Extensions of TVP to nonlinear terms and cyclostationary noise are also outlined.