A Design-Oriented Soft Error Rate Variation Model Accounting for Both Die-to-Die and Within-Die Variations in Submicrometer CMOS SRAM Cells
Bibliographic record
Abstract
Submicrometer static random access memory cells are more susceptible to particle strike soft errors and increased statistical process variations, in advanced nanometer CMOS technologies. In this paper, analytical models for the critical charge variations accounting for both die-to-die and within-die variations are proposed. The derived models are verified and compared to Monte Carlo simulations by using industrial 65-nm CMOS technology. This paper provides new design insights such as the impact of the coupling capacitor, one of the most common soft error mitigation techniques, on the critical charge variability, especially, at lower supply voltages. It demonstrates that two extreme values of this coupling capacitor exist. The first value results in maximum relative variations and the other results in minimum relative variations. Therefore, the circuit designers can utilize these results to design the coupling capacitor to limit the variations under power and performance constraints in early design cycles. The derived analytical models account for the impact of the supply voltage and different particle strike conditions. These results are particularly important for soft error tolerant and variation tolerant designs in submicrometer technologies, especially, for low power operations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".