Quasi-Monte Carlo For Fast Statistical Simulation Of Circuits

Continued device scaling has dramatically increased the statistical variability with which circuit designers must contend to ensure the reliability of a circuit to these variations. As discussed in the introduction to this thesis, traditional process corner analysis is no longer reliable because the variations are numerous and much more complex than can be handled by such simple techniques. Going forward, it is increasingly important that we account accurately for the statistics of these variations during circuit design. In a few special cases, we have analytical methods that can cast this inherently statistical problem into a deterministic formulation, e.g., optimal transistor sizing and threshold assignment in combinational logic under statistical yield and timing constraints, as in Mani et al. (Proc. IEEE/ACM Design Autom. Conf., 2005). Unfortunately, such analytical solutions remain rare. In the general case, some combination of complex statistics, high dimensionality, profound nonlinearity or non-normality, stringent accuracy, and expensive performance evaluation (e.g., SPICE simulation) thwart our analytical aspirations. This is where Monte Carlo methods (Glasserman, Monte Carlo Methods in Financial Engineering, Springer, Berlin, 2004) come to our rescue as true statistical methods.