Channel-Adaptive ADC and TDC for 28 Gb/s PAM-4 Digital Receiver
Bibliographic record
Abstract
A low-power channel-adaptive 28 Gb/s PAM-4 receiver is presented utilizing a predictive analog-to-digital converter (ADC), a successive-approximation-register (SAR) time-to-digital converter (TDC), and a feed-forward equalizer (FFE) in the digital domain. The variable-resolution flash ADC takes advantage of the channel inter-symbol interference (ISI) and can achieve 5.5 bits resolution utilizing only 16 comparators. By reusing the comparators, the ADC can provide a programmable resolution from 2 to 5.5 bits consuming 40 to 90 mW, respectively. The SAR-TDC generates 5 bits timing information that includes 2 bits ISI and 3 bits timing error to achieve a low-latency and low-jitter timing recovery. Subsequently, a three-to-eight programmable tap FFE is used to equalize up to 30-dB loss achieving bit error rate lower than 10-8. FFE is implemented in a field-programmable gate array, and the first three taps are realized in a look-up table (LUT). An offline higher resolution ADC is used to generate the pre-computed values for the LUT. Measured power consumption is 130 mW (excluding digital signal processing) from a 1.2-V power supply with active chip area of 0.2025 mm2in 65-nm technology. Due to programmability on the both ADC resolution and the number of FFE taps according to the channel loss, the receiver enables energy efficiency according to loss compensation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".