Design and fabrication of SOI micro-ring resonators based on sub-wavelength grating waveguides
Bibliographic record
Abstract
Standard silicon photonic strip waveguides offer a high intrinsic refractive index contrast; this permits strong light confinement, leading to compact bends, which in turn facilitates the fabrication of devices with small footprints. Sub-wavelength grating (SWG) based waveguides can allow the fabrication of low loss devices with specific, engineered optical properties. The combination of SWG waveguides with optical micro-resonators can offer the possibility of achieving resonators with properties different from the traditional SOI rings. One important property that SWG rings can offer is decreased light confinement in the waveguide core; this improves the resonator's sensitivity to changes in the cladding refractive index, making the rings ideal for refractive index sensing applications. In this paper, we present the design and experimental characterization of SWG based rings realized on SOI chips without upper cladding (permitting their use as sensors). The fabricated rings offer quality factors in the range of ~1k-6k, depending on SWG parameters. Based on the comparison of experimental and simulated data we expect sensitivities exceeding 383 nm/RIU in water and 270 nm/RIU in air, showing excellent potential for use in sensing applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".