Controlling the Modal Confinement in Silicon Nanophotonic Waveguides through Dual‐Metamaterial Engineering
Bibliographic record
Abstract
Abstract Flexible control of the modal confinement in silicon photonic waveguides is an appealing feature for many applications, including sensing and hybrid integration of active materials. In most cases, strip waveguides are the preferred solution to maximize the light interaction with the waveguide surroundings. However, the only two degrees of freedom in Si strip waveguides are the width and thickness, resulting in limited flexibility in evanescent field control. Here, a new strategy that exploits metamaterial engineering of the waveguide core and cladding is proposed and demonstrated to control the index contrast in the vertical and horizontal directions, independently. The proposed dual‐material geometry yields a substantially increased calculated bulk sensitivity in the air (0.35 RIU [refractive index unit]/RIU) compared to the best case scenario for a strip waveguide (0.3 RIU/RIU). To experimentally demonstrate the potential of this approach, dual‐metamaterial ring resonators operating with the transverse‐magnetic polarized mode in 220‐nm‐thick waveguides with air as upper‐cladding are implemented. Micro‐ring resonators implemented with strip and dual‐metamaterial waveguides exhibit the same measured quality factors, near 30 000. Having similar measured quality factors and better calculated bulk sensitivity than strip waveguides, the proposed dual‐metamaterial geometry stands as a promising approach to control modal confinement in silicon waveguides.
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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) | 0.000 | 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".