6.8 A 36Gb/s Adaptive Baud-Rate CDR with CTLE and 1-Tap DFE in 28nm CMOS
Bibliographic record
Abstract
Baud-rate clock-and-data recovery circuits (CDR) are ubiquitous in recent receiver designs as a means of lowering power consumption by sampling the data only once per UI. To further reduce power, prior works in pattern-based baud-rate PD [1] and FD [2] combine clock & data recovery by sharing comparators between the DFE and the PD. However, both CDRs [1, 2] were manually tuned by sweeping the equalization settings of the CTLE and the comparator levels in order to achieve lock. Since the two settings are correlated, it is time-consuming and tedious to sweep the entire solution space. In this paper, we propose an adaptive engine where the CDR system searches and converges to an optimal lock and sampling point. The proposed scheme, implemented in 28nm CMOS and operating at 36Gb/s, relies on the data eye to autonomously adapt the parameters of a CTLE, a 1-tap DFE, and the PD locking point.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".