A Differential Push-Pull Voltage Mode VCSEL Driver in 65-nm CMOS
Bibliographic record
Abstract
Improving power-conversion efficiency (PCE) of VCSEL drivers is paramount to improve the overall energy efficiency of the entire optical link for high-performance computing and data centers. VCSEL diodes are normally driven single-ended with pseudo-differential current-mode drivers to maintain signal integrity. However, such conventional drivers consume significant power and are often unable to compensate for supply switching noise due to package parasitics at high data-rates. We propose a differential push-pull voltage-mode VCSEL driver to mitigate bondwire parasitics, reduce power consumption, and leverage CMOS process scaling to its maximum advantage. A proof-of-concept prototype in 65-nm CMOS process achieves the highest ever-reported PCE of 18.7 % for VCSEL drivers when normalized to VCSEL slope efficiency. It uses an asymmetric 3-tap rise and fall-based pre-emphasis to achieve a total energy/bit of 1.52 pJ/b at 16 Gb/s with an average optical power output of 1.34 dBm, OMA of 2.1 dBm, and extinction ratio of 5.92 dB.
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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) | 0.001 | 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".