Low-power adaptive edge decision feedback equalizer for serial links with 4PAM signaling
Bibliographic record
Abstract
This paper presents a low-power adaptive edge decision feedback equalizer (DFE) for 10 giga-bits-per-second (Gbps) serial links with 4 PAM (pulse-amplitude-modulation) signaling. Optimal tap coefficients are obtained adaptively using a sign-sign least-mean-square (SS-LMS) algorithm that minimizes the jitter of equalized data. Low-voltage-differential-signaling (LVDS) tap generators that double DFE strength without increasing power consumption are used. Power reduction is also achieved by only activating the tap generator corresponding to the incoming data and sharing slicers for determining data state, the sign of data jitter, and bang-bang phase detection. A frequency locked-loop locked to an external frequency reference and a bang-bang phase-locked loop locked to the edge of equalized data, both sharing the same active inductor ring oscillator with separate frequency and phase tunings, are employed for clock recovery. The effectiveness of the proposed edge DFE is validated using a 10 Gbps 4PAM serial link designed in a 65 nm CMOS technology over a wire channel with 12 dB loss at baud-rate frequency. Simulation results demonstrated that the proposed adaptive edge DFE is capable of achieving 46% vertical opening and 60\% horizontal eye-opening while consuming 26.24 mW.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".