Modeling and Optimization Techniques for Yield-Aware SRAM Post-Silicon Tuning

Abstract

SRAM cell design is driven by the need to satisfy several stability and performance criteria for all cells in the array in an energy-efficient manner. Significant randomness of FET threshold voltages makes achieving this difficult and limits both the minimum cell size and minimum array supply voltage. Post-silicon adaptivity in the form of an adaptive-voltage scheme in a partitioned SRAM array can be used to reduce impact of variability despite lack of any spatial correlation in realizations. This paper develops a novel optimization flow for yield-aware cell sizing and voltage selection under variability given the availability of post-silicon voltage tuning. We formulate a two-stage stochastic optimization problem in which the first-stage decision is to select cell size and possible voltage levels, and the second-stage decision is to assign each partition to an optimal voltage after manufacturing. We develop closed-form statistical models of array margin behavior and yield as a function of V dd , cell size, and array size. We solve the problem using dynamic programming that minimizes power while meeting yield constraints on read, write, and static noise margins. The proposed flow allows designs that are on average 8% and up to 17% more power-efficient than the designs in which voltages are selected uniformly. The results also indicate that at high-yield levels power savings can be up to 32% in the active mode and 71% in the standby mode.