A Clock Duty Cycle Correction Circuitry for Ultra-Wide Frequency Range using Nested Loops
Bibliographic record
Abstract
Clock duty cycle is important in high-speed interface designs, as it directly impacts the transmitted data eye width, thereby increasing the deterministic jitter (DJ) of overall high-speed link. Traditional methods of duty cycle correction (DCC) include an analog negative feedback loop, which is slow, difficult to stabilize and requires a constant clock for the loop to track and maintain the output clock’s duty cycle. Further, analog loops suffer from burst mode issues, where traffic is sporadic, and clocks are shutdown to save the power. We propose a mixed mode nested loop based digital duty cycle correction (DCC) circuitry using analog sensing loop, which overcomes the issues observed in a traditional negative feedback based analog loop. The proposed nested loop solution corrects clock duty cycle over an ultra-wide frequency range with minimal/zero power impact. The proposed solution supports duty cycle corrected clock for burst mode traffic over an ultra-wide operating frequency range. The proposed solution can find its applications in variety of clocking intellectual properties (IP) for its merits. Implemented in $7 \mathbf{n m}$ FinFET technology, the proposed nested loop based digital DCC solution achieves target duty cycle correction range requirements for $500 \mathrm{MHz}-10 \mathrm{GHz}$ frequency range with residual DC error contained within $1 \%$.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".