Optimization of coupling and transmission through finite height SOI photonic crystal slab waveguides
Bibliographic record
Abstract
Transmission and coupling mechanisms in photonic crystal waveguides have been extensively studied in the last few years to optimize photonic crystal designs. Previous numerical results, using 2D FDTD methods have shown that the technique of tapering the photonic crystal lattice can improve coupling efficiencies up to 80%, as compared to 30-40% efficiencies in butt-coupled waveguides. However, for the 3D structures such as photonic crystal slabs, 2D calculations do not take into consideration the losses in the vertical direction and hence 3D simulations are necessary to obtain more accurate results which can be better compared with experimental data. In this paper 2D and 3D FDTD calculation results obtained for a finite height hexagonal silicon photonic crystal slab waveguide (air as top and bottom cladding) with air holes embedded in a silicon dielectric matrix are presented. Various coupling design configurations were investigated using 3D FDTD and coupling efficiencies of 78% in the photonic crystal waveguide and 72% through the output conventional waveguide were obtained for a conventional waveguide of width 3μm coupled to a step tapered PC waveguide on the input and output ports. Furthermore some designs which show excellent efficiencies with 2D calculations are clearly shown to have significant losses in the vertical direction in 3D simulations.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".