Bend Waveguides on Silicon Nanowire Optical Waveguide (SNOW)
Bibliographic record
Abstract
In a previous work, we reported a novel optical waveguide named silicon nanowire optical waveguide (SNOW), which consists of arrayed silicon nanowires acting like an effective-index medium. In this paper, we analyze bend properties of the waveguide and show that small radii bends can be achieved in SNOW structures with low radiation losses. For bend radii of 5 and 2 μm at a wavelength of 1550 nm, the radiation loss per 360° turn is 5 ×10-4and 0.05 dB, respectively. Furthermore, we study the losses for changing the pitch between the nanowires and show that the loss behavior follows effective-index bulk waveguide approximation. Consequently, we show that the bending losses can be optimized by arranging the nanowires pitch size such that the density is higher in the inner side of the bend as compared with the outer side. Effects of wavelength and sidewall roughness on propagation loss are also investigated.
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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) | 0.001 | 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".