Decision-Feedback-Equalizer for 10-Gb/s backplane transceivers for highly lossy 56-inch channels
Bibliographic record
Abstract
This paper presents a decision-feedback-equalizer for 10-Gb/s backplane transceivers for highly lossy channels. Forward equalization is not used in order to avoid noise enhancement. The sampling phase is optimized to achieve maximum signal-to-noise-ratio at the sampling instants. The coefficients of feedback finite-impulse-response filter and the gain of variable-gain-amplifier are obtained automatically by adaptive circuits. The vertical eye opening is almost doubled compared with a conventional decision-feedback-equalizer for a 56-inch channel with heavy loss more than -6.5 dB/GHz. The DFE is implemented in 0.13-mum IBM RF CMOS technologies. Results show an 8-tap DFE can open the highly blurred eye diagram, while a 12-tap conventional DFE can not open it. A 10-tap DFE yields a very clear eye diagram for PRBS-9 test patterns.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".