Edge-Based Adaptation for a 1 IIR + 1 Discrete-Time Tap DFE Converging in $5~\mu$ s
Bibliographic record
Abstract
A 16 Gb/s 1-tap Infinite impulse response (IIR) + 1-tap discrete-time (DT) decision feedback equalizer (DFE) with integrated clock recovery and adaptation is demonstrated in 28 nm FD-SOI CMOS. Using a CMOS phase rotator, 0.7 unit interval (UI) high-frequency jitter tolerance is achieved when operating mesochronously, and over 0.4 UI operating plesiochronously. The half-rate architecture includes a novel 2:1 multiplexer to reduce delay in the IIR feedback path. With a 28 dB loss channel, a BER below 10-12is measured over a 0.32 UI timing window with a TX swing of 0.8 Vpp-diff. Using a 2 Vpp-diff TX swing, a 30 dB loss channel has a measured BER below 10-12over a 0.3 UI timing window. A novel edge-based algorithm adapts both IIR and DT equalizer coefficients using the same high-speed circuitry and signals required for clock recovery. The algorithm utilizes all transitions to inform the adaptation of all equalizer coefficients, thereby providing faster convergence than previously-reported algorithms which await specific patterns. Moreover, the adaptation freezes automatically unless a diverse set of data patterns is received, thereby making the algorithm robust in the presence of poorly-conditioned data. The adaptive DFE converges within 5 μs.
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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 | 0.000 |
| 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) | 0.003 | 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".