Statistical analysis of SRAM cell stability
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
Yield-driven near-threshold SRAM design
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Analyzing static and dynamic write margin for nanometer SRAMs
Proceedings of the 13th international symposium on Low power electronics and design
SRAM dynamic stability: theory, variability and analysis
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
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
Functionality and stability analysis of a 400mV quasi-static RAM (QSRAM) bitcell
Microelectronics Journal
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Static noise margin analysis using butterfly curves has traditionally played a leading role in the sizing and optimization of SRAM cell structures. Heightened variability and reduced supply voltages have resulted in increased attention being paid to new methods for characterizing dynamic robustness. In this work, a technique based on vector field analysis is presented for quickly extracting both static and dynamic stability characteristics of arbitrary SRAM topologies. It is shown that the traditional butterfly curve simulation for 6T cells is actually a special case of the proposed method. The proposed technique not only allows for standard SNM "smallest-square" measurements, but also enables tracing of the state-space separatrix, an operation critical for quantifying dynamic stability. It is established via importance sampling that cell characterization using a combination of both separatrix tracing and butterfly SNM measurements is significantly more correlated to cell failure rates then using SNM measurements alone. The presented technique is demonstrated to be thousands of times faster than the brute force transient approach and can be implemented with widely available, standard design tools.