A 0.41 pJ/Bit 10 Gb/s Hybrid 2 IIR and 1 Discrete-Time DFE Tap in 28 nm-LP CMOS
Bibliographic record
Abstract
An ideal infinite impulse response (IIR) decision feedback equalizer (DFE) can have an effect on wireline received waveforms similar to a continuous-time equalizer, but without the associated amplification of noise and crosstalk. However, an IIR DFE's performance degrades significantly as the feedback loop delay increases. Fortunately, adding a single discrete-time tap can eliminate the degradation. The implementation of a half-rate DFE with two IIR taps and one discrete-time tap is presented here. The two IIR filters have different time constants to accommodate a variety of channel pulse responses having a long tail. The discrete-time tap cancels the first post-cursor inter-symbol interference (ISI) term and alleviates feedback loop timing issues. The DFE can receive data transmitted with a low swing of 150 mVpp-diff through 24 dB of channel loss at half the bitrate while consuming 4.1 mW at 10 Gb/s. Digital foreground calibration of clock phase shifters and offset cancellation is described. The receiver, including the DFE, clock buffers and clock phase adjustment, occupies an area of 8760 μm 2 in an ST 28 nm LP CMOS process.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".