Accurate and Insightful Closed-Form Prediction of Subthreshold SRAM Hold Failure Rate

Abstract

The failure probabilities of industrial SRAM cells fall below the ppm (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> ) range, disqualifying the computational-intensive Monte-Carlo simulations for efficient robustness assessment. Starting from a novel two-dimensional threshold voltage imbalance representation, we propose a new methodology for fast and accurate prediction of the subthreshold SRAM hold stability failure rate. The probability is derived in a closed form which involves the transistor threshold-voltage standard deviations and only requires the two quick DC extractions of the worst- and best-case static noise margins. We validate our approach on a Six-Transistor (6T) bitcell in 28 nm Fully Depleted Silicon-On-Insulator (FD-SOI) CMOS technology. Our method turns out to be especially insightful for comparative and sensitivity analyses, for instance to study the effect of supply voltage downscaling or temperature variations. Finally, we show that the achieved accuracy and the capability of estimating extremely low failure probabilities (down to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−9</sup> ), combined with the important gain in simulation and post-processing cost, makes our methodology attractive compared to other recent modelling works.