Robustness analysis of 6T SRAMs in memory retention mode under PVT variations

Process variability is becoming a major challenge in CMOS design of general and embedded SRAMs in particular due to continuous device scaling. The main problems are the increased static power and reduced operating margins, robustness and reliability. A common way to reduce the static power consumption of an SRAM memory array is to decrease its supply voltage when in memory retention mode. However, this leads to a further reduction in memory robustness. The most common tool for statistical analysis of circuits under process variability is standard Monte Carlo simulation which has been proven to be too expensive when applied on an ultra dense SRAM [l]-[6]. In this paper a statistical robustness analysis method is proposed based on decoupling statistical integration from robustness region determination in the parameter domain. The robustness is estimated with a ∼ 556X speed up relation to Monte Carlo and an error of ∼ 1%.

[1]  Yiyu Shi,et al.  QuickYield: An efficient global-search based parametric yield estimation with performance constraints , 2010, Design Automation Conference.

[2]  Jaijeet S. Roychowdhury,et al.  Rapid Estimation of the Probability of SRAM Failure due to MOS Threshold Variations , 2007, 2007 IEEE Custom Integrated Circuits Conference.

[3]  E. Seevinck,et al.  Static-noise margin analysis of MOS SRAM cells , 1987 .

[4]  Xiaoping Du,et al.  A MOST PROBABLE POINT BASED METHOD FOR UNCERTAINTY ANALYSIS , 2000 .

[5]  E. J. W. ter Maten,et al.  Importance sampling Monte Carlo simulations for accurate estimation of SRAM yield , 2008, ESSCIRC 2008 - 34th European Solid-State Circuits Conference.

[6]  Yiyu Shi,et al.  : Yield Estimation Parametric Yield Estimation for SRAM Cells : Concepts , Algorithms and Challenges , 2010 .

[7]  Jian Wang,et al.  SRAM parametric failure analysis , 2009, 2009 46th ACM/IEEE Design Automation Conference.

[8]  Rajiv V. Joshi,et al.  Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events , 2006, 2006 43rd ACM/IEEE Design Automation Conference.

[9]  Samar K. Saha,et al.  Modeling Process Variability in Scaled CMOS Technology , 2010, IEEE Design & Test of Computers.

[10]  Jaijeet S. Roychowdhury,et al.  An efficient, fully nonlinear, variability-aware non-monte-carlo yield estimation procedure with applications to SRAM cells and ring oscillators , 2008, 2008 Asia and South Pacific Design Automation Conference.

[11]  James Tschanz,et al.  Parameter variations and impact on circuits and microarchitecture , 2003, Proceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451).

[12]  Rob A. Rutenbar,et al.  Digital Circuit Design Challenges and Opportunities in the Era of Nanoscale CMOS , 2008, Proceedings of the IEEE.

[13]  Timothy N. Trick,et al.  A Study of Variance Reduction Techniques for Estimating Circuit Yields , 1983, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[14]  G. Hachtel The simplicial approximation approach to design centering , 1977 .

[15]  E. I. Vatajelu,et al.  Statistical analysis of SRAM parametric failure under supply voltage scaling , 2010, 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR).

[16]  A. Genz Numerical Computation of Multivariate Normal Probabilities , 1992 .

[17]  Yehea I. Ismail,et al.  Accurate Estimation of SRAM Dynamic Stability , 2008, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.