An adaptive edge decision feedback equalizer with 4PAM signalling
Bibliographic record
Abstract
This paper presents an adaptive edge decision feedback equalizer (DFE) with 4PAM signaling. Optimal DFE tap coefficients and threshold voltages for data recovery are obtained adaptively using sign-sign least-mean-square (SS-LMS) algorithms that minimize data jitter. Clock and data recovery is carried out using a dual phase/frequency-locked loop. A 10 Gbps 4PAM serial link has been designed in a 65 nm CMOS technology and analyzed using Spectre from Cadence Design Systems with BSIM4 models. Simulation results demonstrate that the proposed adaptive edge DFE is capable of opening completely closed data eyes at the far end of a backplane channel with -25 dB boud-rate attenuation with 46.5% and 56% horizontal and vertical eye-openings, respectively while consuming 66 mW power.
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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".