Bibliographic record
Abstract
This study presents an adaptive data‐transition decision feedback equaliser (DT‐DFE) with a sign 3 least‐mean‐square (LMS) tap adaptation. Commonly used data‐state (DS) DFE suffers from reduced vertical eye‐opening when consecutive 1's or 0's are encountered. The proposed DT‐DFE performs DFE only when a data transition is detected. It boosts the eye‐opening of the high‐frequency components of data without attenuating the low‐frequency components of data whereas DS‐DFE boosts the eye‐opening of the high‐frequency components of data at the expense of the attenuated low‐frequency components of data. The reference voltages of DS‐DFE is tap‐dependent whereas those of DT‐DFE are tap‐independent and are obtained by conveying consecutive 1's and 0's to the channel in a training phase. The proposed DT‐DFE utilises loop unrolling to detect the occurrence of data transition. The performance of the proposed DT‐DFE is compared with that of DS‐DFE using two 5 Gbps backplane serial links designed in a TSMC 65 nm CMOS technology. Simulation results demonstrate that the eye‐opening of the link with DT‐DFE is 1.54 times that with DS‐DFE. The vertical eye‐opening of the link with DT‐DFE activating tap adaptation on two consecutive state transitions of opposite polarities is 1.2 times that that activates tap adaptation on single state transition. The proposed DT‐DFE is less sensitive to process uncertainty whereas DS‐DFE is prone to process uncertainty with severely deteriorating performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".