Design and Simulation of Deep Nanometer SRAM Cells under Energy, Mismatch, and Radiation Constraints

The usual way of modeling variability using threshold voltage shift and drain current amplification is becoming inaccurate as new sources of variability appear in sub-22nm devices. In this work we apply the four-injector approach for variability modeling to the simulation of SRAMs with predictive technology models from 20nm down to 7nm nodes. We show that the SRAMs, designed following ITRS roadmap, present stability metrics higher by at least 20 % compared to a classical variability modeling approach. Speed estimation is also pessimistic, whereas leakage is underestimated if sub-threshold slope and DIBL mismatch and their correlations with threshold voltage are not considered.

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Four-injector variability modeling of FinFET predictive technology models

Till today CMOS scaling is considered as the best option to achieve higher density, high performance and low power integrated circuits. However, scaling of conventional planar MOSFET in the sub-45nm regime leads to many undesirable short channel effects. FinFET is considered as the suitable candidate for the replacement of conventional planar MOSFETs. In this work, suitability of FinFETs for replacement of planar bulk technology in sub-20nm regime has been verified using Predictive Technology Models. For this purpose, the performance of the FinFET based SRAM cell is compared with conventional planar Bulk based SRAM cell. Moreover, robustness of FinFET based SRAM cell against process, temperature and power supply variations is evaluated and compared with conventional planar based SRAM cell. Our simulation results confirms the suitability of FinFETs for the replacement of conventional planar CMOS technology.

http://ieeexplore.ieee.org/iel7/6624210/6643543/06643584.pdf

Hansel Dsilva

Papers

Variability aware performance evaluation of low power SRAM cell