Analytical Soft Error Models Accounting for Die-to-Die and Within-Die Variations in Sub-Threshold SRAM Cells
Bibliographic record
Abstract
Sub-threshold SRAM cells are attractive because of their low leakage power and low access energy. However, the susceptibility of sub-threshold SRAM cells to soft errors is high due to their low supply voltage, high density, and shrinking geometry. Moreover, the increase in statistical variations in advanced nanometer CMOS technologies poses a major challenge for sub-threshold circuits designers. In this paper, analytical models for the sub-threshold SRAM critical charge variations, which account for both die-to-die (D2D) and within-die (WID) variations, are proposed. The derived models are then compared with Monte Carlo simulations by using industrial hardware-calibrated 65-nm CMOS technology. This paper also provides novel design insights such as the impact of the coupling capacitor, one of the most common soft error mitigation techniques, on the critical charge variability. In addition, it demonstrates that the relative critical charge variability is minimum at a certain temperature value. Then, the circuit designer can employ these results with temperature control techniques to minimize the critical charge variability in the early design cycles, especially, for applications with strict soft error rate (SER) constraints. In Zaddition, the proposed models show that the device sub-threshold swing coefficient can be optimized to minimize the relative critical charge variability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".