Minimum jitter‐based adaptive decision feedback equaliser for giga‐bit‐per‐second serial links
Why this work is in the frame
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Bibliographic record
Abstract
This study presents a minimum jitter‐based adaptive decision feedback equaliser (DFE) for giga‐bit‐per‐second (Gbps) serial links. The adaptation in search for the optimal tap coefficients of DFE is carried out with the objective to minimise data jitter at the edge of data eyes. Jitter minimisation is achieved by adjusting the slope of the DFE that counteracts that of the channel. The effectiveness of the proposed adaptive DFE is evaluated by embedding the DFE in a 2 Gbps serial link. The data link is analysed using Spectre from Cadence Design Systems with BSIM4 device models. Simulation results demonstrate that the proposed adaptive DFE is capable of opening closed data eyes with 83% vertical opening, 68% horizontal opening and 16% data jitter over 1 m FR4 channel while consuming 15.45 mW.
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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 | 0.001 | 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 it