Sub-wavelength grating components for integrated optics applications on SOI chips
Bibliographic record
Abstract
In this paper we demonstrate silicon on insulator (SOI) sub-wavelength grating (SWG) optical components for integrated optics and sensing. Light propagation in SWG devices is studied and realized with no cladding on top of the waveguide. In particular, we focused on SWG bends, tapers and directional couplers, all realized with compatible geometries in order to be used as building blocks for more complex integrated optics devices (interferometers, switches, resonators, etc.). Fabricated SWG tapers for TE and TM polarizations are described; they allow for connecting SWG devices to regular strip waveguides with loss lower than 1 dB per taper. Our SWG directional coupler presents a very compact design and a negligible wavelength dependence of its crossover length (and as a consequence of its coupling coefficient, κ), over a 40 nm bandwidth. This wavelength flatten response represents a bandwidth enhancement with respect to standard directional couplers (made using strip or rib waveguides), in particular for the TE mode. SWG bends are demonstrated, their loss dependence on radius is analyzed, and fabricated bends have a loss in the range 0.8-1.6 dB per 90 degrees bend. Simulated and measured results show promise for large-scale fabrication of complex optical devices and high sensitivity sensors based on SWG waveguides with engineered optical properties, tailored to specific applications.
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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) | 0.003 | 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".