Ultra-Small GaAs-Photonic-Crystal-Slab-Waveguide-Based Near-Infrared Components: Fabrication, Guided-Mode Identification, and Estimation of Low-Loss and Broad-Band-Width in Straight-Waveguides, 60°-Bends and Y-Splitters
Bibliographic record
Abstract
We designed and fabricated ultra-small photonic-crystal (PC)-waveguide (WG)-based components of straight-WGs, 60°-bends and Y-splitters, utilizing single-line-defect in the air-bridge type GaAs PC, and revealed their optical properties in detail by observing a transmittance (T) spectrum over a broad wavelength-region from 850 to 1600 nm. For this purpose, we developed a halogen-lamp-based spectrometer for an ultra-thin slab-sample, which has turned out very useful. We also calculated the corresponding T-spectrum by using three-dimensional finite-difference-time-domain method. In all cases where comparison is possible, the observed spectrum agrees essentially with the calculated one. Owing to these broad spectra, we have unambiguously identified the high-T region due to single guided-mode in the respective components. Comparisons of the spectrum between different samples or components, such as the straight-WGs with much different lengths, or the straight-WG and the sharp bend have also helped to reasonably interpret the observed spectra, allowing a comprehensive understanding of optical properties of those components. The propagation loss and the band width in each component could also be estimated from the comparison. The loss is estimated as 1.5 ±0.5 dB/mm for the straight-WG, while less than 1 dB/bend, if it exists, for the bend WG, and both band-widths are broader than 40 nm. We also find that the Y-splitter is capable of dividing light into two ports almost equally in intensity with a total transmittance of more than 85%; the band-width is broader than 40 nm. As a result, we have found that all those should be useful as key components in future ultra-fast planar optical integrated circuits.
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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 | 0.001 | 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".