Fast-Locking Burst-Mode Clock and Data Recovery for Parallel VCSEL-Based Optical Link Receivers
Bibliographic record
Abstract
A burst-mode clock-and-data-recovery (CDR) system for a multi-channel vertical-cavity surface-emitting laser (VCSEL)-based non-return-to-zero (NRZ) optical link’s quarter-rate receivers is presented, that utilizes proxy timing recovery for fast turn-on time. The proxy timing recovery scheme takes advantage of correlated data jitter over parallel optical lanes typically deployed in a data center. Rapid timing recovery of the burst-mode channel is enabled by incrementing/decrementing its phase rotator (PR) control code during idle periods using phase updates from an always-active channel in the link. This work also presents circuit-design techniques to reduce power dissipation during idle times while still enabling fast turn-on time. Simulated in 65 nm CMOS technology, the proposed CDR consumes only 19.5 mW per channel while operating at 10 Gbps/ch and 0.58 mW during its idle-state. Simulation results are presented for the turn-on time with the proposed technique and compared against the turn-on time of a conventional receiver. The proposed technique allows the CDR to lock within 26 unit intervals (UIs) from when it is powered on irrespective of a 1000 ppm frequency offset between the incoming data and the CDR’s reference clock. The complete CDR of each channel occupies an area of 0.045 mm2. The proposed scheme introduces only 1.3 % of area and 2.6 % of on-state power overhead while reducing idle-time power dissipation by 97 %.
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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 | 0.000 | 0.000 |
| 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".