Early, Accurate and Fast Yield Estimation through Monte Carlo-Alternative Probabilistic Behavioral Analog System Simulations

Monte Carlo analysis has so far been the corner stone for analog statistical simulations. Fast and accurate sim-ulations are necessary for stringent time-to-market, design for manufacturability and yield concerns in the analog do-main. Although Monte Carlo attains accuracy, it does so with a sacrifice in run-time for analog simulations. In this paper, we propose a fast and accurate probabilistic simula-tion method alternative to Monte Carlo using deterministic sampling and weight propagation. We furthermore propose accuracy improvement algorithms and a fast yield calcu-lation method. The proposed method shows accuracy im-provement combined with a 100-fold reduction in run-time with respect to a 1000-sample Monte Carlo analysis.