Low Temperature SiN Waveguides Optimization for Photonic Platform
Bibliographic record
Abstract
Integration of SiN on Si photonics platform becomes attractive for 3D integration of different waveguide levels in an optical routing circuit, and can be used for the realization of athermal devices needing specifics group index or group velocity dispersion. This paper is focused on the determination of the optical properties of different SiN deposited by PVD (Plasma Vapor Deposition) and PECVD (Plasma Enhanced Plasma Vapor Deposition) equipment with temperatures lower than 400°C to keep the compatibility with CMOS process. A set of designs including optical routing basic building blocks such as bends, MMI splitters and asymmetric Mach-Zehnder interferometer has been designed to determine propagation losses and propagation constants with high accuracy, for a large spectral range, around 1.31 μm and 1.55 μm [1]. Devices were manufactured on the STMicroelectronics DAPHNE (Datacom Advanced Photonics Nanoscale Environment) 300 mm Photonic R&D platform [2]. Comparison of experimental data with theoretical models will be made. Especially, we discuss the development of Finite Difference Full Vectorial mode solvers [3]-[4] coupled with the Mode Matching method. This method, used to simulate the propagation of light, also allows to evaluate side wall roughness contributions, Rayleigh scattering and absorption due to 2ndharmonic vibrations of Si-OH and N-H bonds.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".