4×, 3‐level, blind ADC‐based receiver
Bibliographic record
Abstract
The design of a 4× blind analogue‐to‐digital converter (ADC)‐based receiver implemented in 65 nm CMOS technology is presented. The ADC, which has three levels with two adjustable thresholds, effectively implements a speculative decision‐feedback equaliser. By reducing the ADC resolution and by simplifying the digital clock and data recovery design, the power consumption is reduced by a factor of 2 compared with previous works. Measurement results confirm a bit error rate of <10 − 12 at 5 Gbit/s with a high‐frequency jitter tolerance of 0.39 and 0.31 UI pp for a 9.3 and a 12.9 dB FR4 channel, respectively. The entire receiver consumes 63 and 86 mW for the respective channels.
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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 | 0.000 | 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 | 0.000 | 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".