High-repetition-rate electro-optic frequency combs using cascaded silicon phase modulators
Bibliographic record
Abstract
We explore what we believe to be novel methods to improve the spacing and flatness of comb lines in an electro-optic frequency comb using cascaded silicon phase modulators. Our analysis extends to various factors influencing the comb spectral symmetry: phase modulation nonlinearity, PN-junction transient response, and waveguide group dispersion. We confirm the feasibility of harnessing these diverse effects to fine-tune the comb spectral shape to enhance uniformity when using cascaded phase modulators. The comb spacing in our experimental solution is adjustable and can be expanded to a maximum of 37.5 GHz. Targeting frequency spacing of 37.5 GHz and a flatness of 6 dB, we can achieve a seven-line frequency comb. This demonstration represents the highest repetition rates ever reported for silicon modulators. For instance, such a comb could carry a super-channel signal of up to 300 Gbaud (i.e., 75 Gbaud × 4) per comb line. Improving spectral uniformity extends comb frequency span, further enhancing the advantages of employing cascaded modulators in the generation of integrated electro-optic frequency combs.
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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 | 0.000 | 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".