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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-8</sup> . 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 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in 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 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 | 0.001 |
| 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".