Diffractive sidewall grating coupler: towards 2D free-space optics on chip
Bibliographic record
Abstract
Silicon photonics has been the subject of intense research efforts. In order to implement complex integrated silicon photonic devices and systems, a wide range of robust building blocks is needed. Waveguide couplers are fundamental devices in integrated optics, enabling different functionalities such as power dividers, spot-size converters, coherent hybrids and fiber-chip coupling interfaces, to name a few. In this work we propose a new type of nanophotonic coupler based on sidewall grating (SIGRA) concept. SIGRAs have been used in the Bragg regime, for filtering applications, as well as in the sub-wavelength regime in multimode interference (MMI) couplers. However, the use of SIGRAs in the radiation regime has been very limited. Specifically, a coarse wavelength division multiplexer was proposed and experimentally validated. In this work we study the use of SIGRAs in the diffractive regime as a mean to couple the light between a silicon wire waveguide mode and a continuum of slab waveguide modes. We also propose an original technique for designing SIGRA based couplers, enabling the synthesis of arbitrary radiation field profile by Floquet- Bloch analysis of individual diffracting elements while substantially alleviating computational load. Results are further validated by 3D FDTD simulations which confirm that the radiated field profile closely matches the target design field.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".