Soft-error Monte Carlo modeling program, SEMM
IBM Journal of Research and Development - Special issue: terrestrial cosmic rays and soft errors
Statistical analysis of SRAM cell stability
Proceedings of the 43rd annual Design Automation Conference
Proceedings of the 43rd annual Design Automation Conference
Analytical modeling of SRAM dynamic stability
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Proceedings of the conference on Design, automation and test in Europe
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Final-value ODEs: stable numerical integration and its application to parallel circuit analysis
Proceedings of the 2009 International Conference on Computer-Aided Design
Proceedings of the 47th Design Automation Conference
On the efficacy of write-assist techniques in low voltage nanoscale SRAMs
Proceedings of the Conference on Design, Automation and Test in Europe
A black box method for stability analysis of arbitrary SRAM cell structures
Proceedings of the Conference on Design, Automation and Test in Europe
On the impact of gate oxide degradation on SRAM dynamic and static write-ability
Proceedings of the 16th Asia and South Pacific Design Automation Conference
Variation-aware static and dynamic writability analysis for voltage-scaled bit-interleaved 8-T SRAMs
Proceedings of the 17th IEEE/ACM international symposium on Low-power electronics and design
Quantifying Dynamic Stability of Genetic Memory Circuits
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Functionality and stability analysis of a 400mV quasi-static RAM (QSRAM) bitcell
Microelectronics Journal
Proceedings of the 2013 ACM international symposium on International symposium on physical design
Leveraging sensitivity analysis for fast, accurate estimation of SRAM dynamic write VMIN
Proceedings of the Conference on Design, Automation and Test in Europe
| Hi-index | 0.00 |
Technology scaling in sub-100nm regime has significantly shrunk the SRAM stability margins in data retention, read and write operations. Conventional static noise margins (SNMs) are unable to capture nonlinear cell dynamics and become inappropriate for state-of-the-art SRAMs with shrinking access time and/or advanced dynamic read-write-assist circuits. Using the insights gained from rigorous nonlinear system theory, we define the much needed SRAM dynamic noise margins (DNMs). The newly defined DNMs not only capture key SRAM nonlinear dynamical characteristics but also provide valuable design insights. Furthermore, we show how system theory can be exploited to develop CAD algorithms that can analyze SRAM dynamic stability characteristics three orders of magnitude faster than a brute-force approach while maintaining SPICE-level accuracy. We also demonstrate a parametric dynamic stability analysis approach suitable for low-probability cell failures, leading to three orders of magnitude runtime speedup for yield analysis under high-sigma parameter variations.