An efficient, fully nonlinear, variability-aware non-Monte-Carlo yield estimation procedure with applications to SRAM cells and ring oscillators

Failures and yield problems due to parameter variations have become a significant issue for sub-90-nm technologies. As a result, CAD algorithms and tools that provide designers the ability to estimate the effects of variability quickly and accurately are being urgently sought. The need for such tools is particularly acute for static RAM (SRAM) cells and integrated oscillators, for such circuits require expensive and high-accuracy simulation during design. We present a novel technique for fast computation of parametric yield. The technique is based on efficient, adaptive geometric calculation of probabilistic hypervolumes subtended by the boundary separating pass/fail regions in parameter space. A key feature of the method is that it is far more efficient than Monte-Carlo, while at the same time achieving better accuracy in typical applications. The method works equally well with parameters specified as corners, or with full statistical distributions; importantly, it scales well when many parameters are varied. We apply the method to an SRAM cell and a ring oscillator and provide extensive comparisons against full Monte-Carlo, demonstrating speedups of 100--1000x.