High-performance low-power sensing scheme for nanoscale SRAMs
Bibliographic record
Abstract
SRAMs in nanoscale CMOS technology suffer from plethora of design challenges such as increased process variation, increased leakage current and variation in the cell current that threatens the reliability of sensing scheme. These issues coupled with continuous increase in the SRAMs size, requires additional techniques and treatments such as read-assist techniques to ensure fast and reliable read operation. In this study, the authors address these concerns and propose a novel read-assist sensing scheme. The circuit is simulated using Spectre in 65 nm CMOS technology. Simulation results showed an increased sensing speed, lower power dissipation and enhanced SRAM dynamic cell stability. A complete comparison is made between the proposed scheme, the conventional circuit and another state of the art design, which shows speed improvement of 55.34, 66.01% and power reduction of 21.33, 89.09% with respect to conventional sense amplifier and the referenced scheme, respectively. These enhancements are at the expense of negligible area overhead. Also, the proposed scheme enables one to reduce the cell's VDD by 227 and 345 mV for the same operating frequency with respect to conventional and referenced circuits, respectively. This results in leakage power reduction of 19.7 and 30% which constitutes a considerable portion of overall power dissipation in nanoscale SRAMs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".