A differential push-pull voltage mode driver for vertical-cavity surface emitting laser
Bibliographic record
Abstract
The unabated demand for data communication has led to a rapid growth in warehouse-sized datacenters where high-end servers transfer terabytes of data per second between the racks using optical data links. Vertical cavity surface-emitting laser (VCSEL) based optical links are widely popular in such datacenters for short-reach (< 300 m) interconnects due to their compact footprint, low cost, ease of integration with multimode fiber and flexibility in implementing arrays to achieve high aggregate data rate. Improving power-conversion efficiency (PCE), defined as the ratio of desired output optical power to the total electrical power of VCSEL driver, is paramount to improve the overall energy efficiency of the entire optical link. 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 complementary meral-oxide semiconductor (CMOS) process scaling to its maximum advantage. A proof-of-concept prototype in a 65nm CMOS process achieves the highest reported PCE to-date 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 efficiency of 1.52 pJ/b at 16 Gb/s with an average optical power output of 1.34 dBm, optical modulation amplitude (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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".