Silicon nanowire optical waveguide (SNOW)
Bibliographic record
Abstract
In this paper, we propose a novel optical waveguide consisting of arrays of silicon nanowires in close proximity. We show that such a structure can guide an optical mode provided the electric field is polarized along the length of the nanowires. Furthermore, such guidance can happen even if the nanowires are arranged randomly albeit at a higher scattering loss. On the other hand, high radiation losses are observed if the electric field is polarized in the transverse direction to the nanowires. We calculate the optical radiation loss for different structures using Finite Difference Time Domain (FDTD) method. We also show that the arrayed nanowire region can be approximated using an effective index bulk waveguide. The approximation allows for design and optimization of optical structures using integrated optics methodology resulting in significant savings in time and resources. The advantage of the proposed waveguide structure is that it allows for increased optical confinement while using the enhanced optical interactions of nanowire structures compared to single nanowire photonic waveguide for diameters smaller than 100 nm. For a diameter of 50 nm for the silicon nanowire, an optical confinement factor of 33 % was achieved in the proposed waveguide as opposed to 0.1 % that is achieved for a single nanowire photonic waveguide. A radiation loss of 0.12 cm(-1) is achieved for nanowires of the same diameter spaced 75 nm apart. While our analysis is done on silicon nanowires at 1550 nm, the proposed structures can be extended to other materials and wavelength regimes also.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".