ADAMIN: Automated, accurate macromodeling of digital aggressors for power and ground supply noise prediction
Estimating interference from large digital blocks, and its effect on on-chip power-distribution networks, is extremely important in deep submicron digital and mixed-signal IC design, especially for systems-on-a-chip. In this paper, we present automated extraction techniques that can be used to generate families of small, time-varying macromodels of digital cell libraries from SPICE-level descriptions. Our automated digital aggressor macromodeling for interference noise (ADAMIN) approach is based on importing and adapting the time-varying Pade method, for linear time-varying model reduction, from the mixed-signal macromodeling domain. Our approach features naturally higher accuracy than previous ones and, in addition, offers the user a tradeoff between accuracy and macromodel complexity. Extracted macromodels capture a variety of noise interference mechanisms, including IR and L(dI/dT) drops for power rails. Using ADAMIN as a core, it is expected that library-characterization methodologies will evolve to include extracted, accurate-by-construction interference noise macromodels for digital cell blocks. Experimental results indicate speedups of several orders of magnitude over full SPICE-level circuits, with prediction accuracies considerably superior to those from commonly-used current-source-based aggressor models.
aggressor supply noise interference digital switching glitch substrate noise victim