Comparative analysis of process variation impact on flip-flops soft error rate
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it