Bibliographic record
Abstract
This work focuses on the practical aspects of high speed baud-rate clock and data recovery (CDR). Baud-rate CDRs reduce the number of clock sampling phases compared to edge-sample phase detector (PD) based CDRs. These CDRs do not require transition samples in addition to the data samples for timing information. Baud-rate CDRs exploit other properties of the incoming data for timing information. Typical baud-rate CDRs rely on specific patterns for timing recovery. However, minimum mean squared error (MMSE) PD based CDRs rely on the slope and error information for timing recovery and therefore are not pattern dependent. A modified form of MMSE simplifies the conventional MMSE algorithm for NRZ data such that only the slope information is required. Three different slope detection techniques are presented: one with an integrate and dump, one with an active filter and the other with a passive filter. The passive filter is most suitable for slope detection at high-speeds. A prototype passive filter in 0.18 pin CMOS is implemented and tested upto 10-Gb/s and consumes 21.6 mW including a pre-amplifier stage. A half-rate modified MMSE PD-based CDR architecture using the passive slope detector is proposed and compared with a conventional edge-sample PD based CDR using identical circuit blocks. Simulations predict improved jitter performance for the proposed technique and similar power consumptions for the two techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".