Comparative analysis of process variation impact on flip-flops soft error rate
Bibliographic record
Abstract
Due to CMOS technology scaling, devices are getting smaller, faster, and operating at lower supply voltages. The reduced capacitances and power supply voltages and the increased chip density to perform more functionality result in increasing the soft errors and making them one of the essential design constraints at the same level as delay and power. Even though the impact of process variations on the performance and the power consumption has been investigated by many researchers, its impact on soft errors has not been paid enough attention. This impact is investigated in this paper for 65-nm CMOS technology. The soft error yield is defined in this paper similar to the timing yield and the power yield. This paper shows that the soft error yield of the sense-amplifier based flip flop (SA-FF) is very poor. Therefore, soft error mitigation techniques are required when using this flip-flop topology. The semi-dynamic flip-flop (SD-FF) exhibits the best soft error yield behavior with a very high performance at the expense of large power requirement. Finally, some design insights are proposed to guide flip-flops designers to select the best flip-flop topology that satisfies their specific circuit soft error rate constraints.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".