Decision Feedback Equalizer Architectures With Multiple Continuous-Time Infinite Impulse Response Filters
Bibliographic record
Abstract
Decision feedback equalizer (DFE) architectures with varying numbers of discrete-time taps and continuous-time infinite impulse response (IIR) filters are compared for use in typical wireline channels. In each case, the DFE coefficients are optimized to minimize a cost function that equally weights both jitter and vertical eye opening. Even when some reflections are present (e.g., backplane channels) continuous-time IIR taps can be effective if their filter coefficients are properly optimized. Using a DFE architecture with only two IIR filters provides adequate results for both a 26-dB loss coax cable and a 16" FR-4 backplane channel at 10 Gb/s while keeping the DFE complexity low. Furthermore, the implementation and experimental results of a DFE with multiple (three) IIR filters is reported. Fabricated in a 0.13 μm CMOS process, the DFE consumes 17.3 mW from a 1.2 V supply. A bit error rate (BER) of 10-12was achieved at a data rate of 3.7 Gb/s.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".