Analysis of Wave-Pipelined Domino Logic Circuit and Clocking Styles Subject to Parametric Variations
Bibliographic record
Abstract
In recent years, wave-pipelined domino logic has received much attention as a means to implement high-speed circuits. However, this logic is vulnerable to parametric variations and the situation will degrade as technology scales down. In this paper, statistical timing relations are developed for characterizing performance impacts of parametric variations in different wave-pipelined domino circuits and clocking styles. Analytic results show that a wave pipeline built with a footless nonblocking domino cell accumulates timing variations due to parametric variation along the pipeline. Thus performance reduces with pipeline size as variations accumulate. On the other hand, wave pipelined footed blocking domino logic is less sensitive to parametric variations. Simulation results of a 6-stage wave pipeline using footed blocking domino cells in 130 nm technology also demonstrate the advantages of this logic style both in performance and power consumption.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".