Description

In energy-constrained applications, SRAM systems operating in the subthreshold region are often deployed to reduce power consumption. Subthreshold SRAM designs, however, confront numerous challenges, such as susceptibility to process variation and reduced ON–OFF current ratio. Statistical modeling of the variation in cell stability is critical in SRAM design, especially, for designs operating in the subthreshold region, where the process and temperature variations are the most pronounced. In this paper, statistical models for estimating the static noise margins (SNMs) of SRAM cells are built from the perspective of a shifted voltage transfer characteristic. Read (hold) SNM of a subthreshold 8T cell is analyzed. It is shown that the distribution of a single-sided read SNM is a weighted sum of several normal distributions instead of a regular Gaussian distribution. The proposed statistical model is verified with simulation results in 65-nm technology. Furthermore, to mitigate performance and yield degradation, an adaptive body biasing circuit is developed. It is demonstrated through simulation that, with a negligible area and power overhead, the proposed circuit achieves a 15% improvement in the worst case read SNM.