A pattern-guided adaptive equalizer in 65nm CMOS
Bibliographic record
Abstract
The use of adaptive equalizers at the front end of receivers is becoming a necessity as the data rates increase without channel improvements. Adaptive equalizers can be implemented using data-aided or non-data-aided schemes, with the latter requiring less area and power. Previous non-data-aided adaptive schemes implement an asynchronous analog algorithm where the power spectrum of the received signal is checked for balance around a threshold frequency. Similarly, proposes a digital adaptive algorithm which is based on the detection of specific 5-bit patterns. In all three works, however, adaptation is provided only for equalizers with a single coefficient, which are suitable for well-behaved channels. In contrast, this paper presents a digital adaptive engine for an equalizer with two coefficients: one adjusting the equalizer gain at the Nyquist frequency (fN) and one at fN/2. Furthermore, the proposed engine is asynchronous; it can function when driven by a blind clock at the receiver. This is useful as it allows the adaptation process to start even when the CDR has not yet achieved lock. This also avoids a deadlock situation where the CDR and equalizer require simultaneous access to the equalized data and the recovered clock. Our measured results of the proposed adaptive equalizer in 65nm CMOS confirm that the adaptation converges to within 2.6% of the optimal vertical eye opening in less than 400 μs for two different channels at a data rate of 6 Gb/s with a 25,000 ppm frequency offset.
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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 | 0.001 | 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".