Synthesis of Planar Reflective Gratings for Silicon Interconnects
Bibliographic record
Abstract
The design and simulations of planar reflective gratings for building optical interconnects in silicon‐on‐insulator (SOI) were studied for a range of silicon core thicknesses of 0.1 to 10 μm. The verticality of the grating facets has been shown to be the main contributing factor to the cumulative crosstalk in thick silicon cores. The dispersion property of the slab was found to limit the minimal thickness of the core for polarization‐insensitive gratings. The effects of polarization‐dependent confinement on optical crosstalk were studied. The findings were used to design and simulate a polarization‐insensitive 18‐channel coarse wavelength division demultiplexer (CWDM) with a free spectral range of over 600 nm. The CWDM demultiplexer uses a 1.7 μm silicon core and combines a shallow‐etch tapered rib structure and multimode silicon channels to produce box‐like passbands for integrated receiver applications. The diffraction grating was constructed using double astigmatic point design with phase‐corrected grating facets to reduce astigmatism. Optical properties of the planar gratings have been simulated using quasivectorial diffraction grating theory. The simulation results confirm that there is high diffraction efficiency and low optical crosstalk over the entire range of operation. Applications of planar silicon gratings to the synthesis of silicon interconnects are discussed.
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 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".