Adaptive data‐transition decision feedback equaliser with edge emphasis
Bibliographic record
Abstract
Abstract Adaptive data‐transition (DT) decision feedback equalisation (DFE) with edge‐emphasised (EE) taps is presented. DFE is performed when DT is detected using a loop‐unrolling algorithm. EE‐taps provide enhanced equalisation at the edges of data eyes, where the impact of channel impairment is most severe, and adequate equalisation between the edges and centre of data eyes to achieve both minimum jitter and maximum vertical eye‐opening of equalised data. Reference voltages for measuring the DFE error signal are adjusted to further improve the vertical eye‐opening of equalised data. The effectiveness of the proposed DFE is investigated using the simulation results of a 10 Gbps (gigabits per second) link over a backplane channel with −23.9 dB channel loss at the baud‐rate in TSMC 65 nm 1.2 V CMOS technology. Simulation results show that DT‐DFE with EE‐taps improves vertical eye‐opening by 3.9 times and lowers data jitter by 4.86 times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 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".