The impact of timing yield improvement under process variation on flip-flops soft error rate
Bibliographic record
Abstract
In deeply pipelined synchronous systems, any violation of the timing constraints of the flip-flops can cause the overall system to malfunction. Due to CMOS technology scaling, increased process variations result in a large delay variability causing unacceptable loss in the timing yield. Several variation tolerant techniques are introduced to mitigate this variability challenge by improving the timing yield. In the mean time, devices are getting smaller, faster, and operating at lower supply voltages. These reduced capacitances and power supply voltages combined with the increased chip density to perform more functionality increase the soft errors susceptibility and make it one of the essential design challenges. Moreover, there are many flip-flops topologies that vary in their relative performance and power consumption which make the selection decision very difficult to flip-flops designers especially under variability and soft errors challenges. Therefore, a comparative analysis between these different flip-flops topologies considering these scaling challenges is beneficial to guide the flip-flops designers in selecting the best topology for their specific application constraints. This paper presents a comparative analysis of the timing yield improvement impact on flip-flops soft error rate by using the STMicroelectronics 65-nm CMOS technology. The analyzed flip-flops are compared for power and power-delay product (PDP) overheads to achieve this timing yield improvement. Then, they are compared for the soft error susceptibility. Finally, it is shown that the timing yield improvement improves the flip-flops soft error immunity significantly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".