Frequency-Dependent Variations in DAC ENoB: Predicting Accurate High-Speed Eye-Diagrams
Bibliographic record
Abstract
While previous digital-to-analog converter (DAC) simulation models predict frequency-dependent effective number of bits (ENoB) well, the time domain distortions are exaggerated. We introduce a model preserving ENoB accuracy and it improves the treatment of time-domain behavior. We enhance model accuracy by filtering out spurious frequency content from aliasing and distortion that mask true behavior. We validate frequency-dependent ENoB for three commercial DACs by comparing our model output to known performance. We run a back-to-back electrical experiment to assess the accuracy of the model to predict bit error rate (BER) for baud rates from 70 to 120 Gbaud. We show that, unlike our model, the previous model leads to predictions of eye-opening and BER that are overly pessimistic at higher baud rates. We analyze the influence of oversampling, showcasing the trade-offs between the accuracy of the expected ENoB and the computational complexity. This work provides a comprehensive tool for understanding the constraints imposed by DACs, facilitating the optimization of next-generation optical communication systems.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".