SWG-designed MMI waveguides for dual and multi-beam splitting, beam position-shifting, and focusing purposes
Bibliographic record
Abstract
In this research, subwavelength grating (SWG) nanostructures with different periodic configurations are designed on a slab dielectric waveguide and theoretically studied for creating beam splitting, position-shifting, and focusing effects, using Comsol Multiphysics as the simulation tool. Su8 with a refractive index (n) of 1.585 is considered as the core material for the dielectric waveguide, which has a lateral and longitudinal dimension of 3 and 6 um, respectively. Uniform and nonuniform rows and columns of nanoholes with diameters of 90 nm are considered as the diffractive design elements. We took advantage of the multimode interference (MMI) phenomenon caused by periodic arrays of nanoholes as SWG structures, which are engineered to induce the desired effects. The power transmission efficiencies of the SWG-designed MMI waveguides are calculated in the wavelength range of 500-1200 nm. The efficiencies are high for the major part of the studied spectrum and reach a maximum of ~97% at 1200 nm for some designs. Also, the refractive index contrasts between the effective index (neff) and the ideal parabolic model (npar) are shown for the conventional MMI SU8 waveguide within a wavelength range of 700-1000 nm. It can be clearly seen that the contrast is minimum for λ = 700nm, and increases with wavelength, showing the multimode interference effect is optimum at 700 nm and deteriorates as the wavelength increases. Modal phase error (MPE) estimated for m=5 and different wavelengths revealed that the MMI device can have a fairly high performance within the whole studied wavelength range for a maximum mode number of 3. Additionally, the field intensity distributions calculated for the design with the beam splitting effect for different wavelengths reflected that the effect has a broadband characteristic.
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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) | 0.001 | 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".