Edge-Based Adaptation for a 1 IIR + 1 Discrete-Time Tap DFE Converging in $5~\mu$ s
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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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-12</sup> is 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-12</sup> over 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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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 | 0.000 | 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 | 0.001 |
| 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