Linearity Enhancement for Inverter-Based Optical Receivers Employing Active Feedback
Bibliographic record
Abstract
The nonlinearity of optical receivers mainly depends on the final stages of the main amplifier. The Cherry-Hooper (CH) amplifier is popular due to its broad bandwidth. However, CH amplifiers may not offer sufficient linearity, especially for P AM-4 optical links. Previously, the authors designed a highly linear PAM-4 optical receiver using$g_{m}/g_{m}$inverter-based amplifiers known for their superior linearity. To compensate for the lower bandwidth of the$g_{m}/g_{m}$amplifiers, the interleaving active feedback (IAFB) technique was applied. However, for sub-maximal input voltage amplitudes, the previously proposed design did not give superior linearity over the open-loop structure without IAFB. This paper addresses this issue by modifying the active feedback loops using voltage dividers. Our proposed design operates at a 50 Gb/s data rate with a 1 V supply and achieves a total harmonic distortion (THD) of less than 0.6% for a 500$\text{mV}_{\mathrm{P}\mathrm{P}}$output swing.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".