Cold-resonance-mediated self-stabilization of Kerr frequency combs in a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Si</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">N</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow></mml:math> microring resonator
Bibliographic record
Abstract
Kerr frequency combs (KFCs) generated from continuous-wave pumped microresonators have been vastly exploited for a plethora of applications. Along with an appreciable bandwidth, most of the applications demand a stable and coherent frequency comb, which is a challenging quest. Several complex experimental approaches were reported to attain stable frequency combs. In this paper, we report an innovative and simple approach to achieve stabilized KFCs in a ${\text{Si}}_{3}{\text{N}}_{4}$ racetrack microring resonator. Intensive numerical simulations reveal an enhancement of the comb bandwidth when the temperature is reduced slightly lower than the room temperature. The maximum temperature rise due to the propagating dissipative Kerr soliton (DKS) has also been studied through finite element simulations. Through homogeneous steady-state analysis we validate that the stability of a single DKS state is enhanced at the temperatures reported in this paper. We believe that the proposed thermal route may help in reducing the complex experimental procedures for stabilization of KFCs.
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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 | 0.000 | 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".