An Efficient Uncertainty- and Skew-aware Methodology for Clock Tree Synthesis and Analysis
Bibliographic record
Abstract
The effect of variations (process, voltage, temperature, and crosstalk) on circuit delay is increasing with technology scaling. As a result, the timing uncertainty of clock signal is increasing. In this paper, an efficient methodology for clock timing uncertainty- and skew-aware clock tree synthesis and analysis is proposed. We first present a statistical and less pessimistic methodology (as compared to the traditional static timing analysis (STA) methodology) for computing the clock timing uncertainty under the impact of parameter variations (process, voltage, temperature, and crosstalk). We also devise a technique to synthesize a clock tree that has zero skew and on which uncertainty can be computed efficiently. Finally, using the proposed uncertainty analysis algorithm, a post-processing scheme to reanalyze the critical paths reported by traditional STA is presented. We applied our zero-skew tree synthesis algorithm on a real industrial design. With our analysis methodology, the worst-case timing uncertainty on this tree was reduced from 388ps (used by traditional STA tools) to 63ps
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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".