Visible and near-infrared photonic components library based on silicon nitride platform
Bibliographic record
Abstract
In this paper we present a library of photonic components based on silicon nitride on insulator (SiNOI) waveguide platform. SiNOI is CMOS compatible technology hence it offers mass-scale and low-cost fabrication. It also exhibit much lower propagation losses and thermo-optical coefficient when compared to silicon on insulator (SOI) technology. In addition, it is more tolerant to fabrication tolerance and have wide transparency range from visible to mid-infrared. The SiNOI platform consists of a 400 nm thick SiN layer with 4.5 μm buried silicon dioxide oxide and 3 μm oxide cladding. The library includes single mode waveguides, bend waveguides, power dividers (directional couplers and multimode interferometers), strip to slot mode converters and grating couplers. Components for both the near infrared wavelength at λ=1550 nm and the visible wavelength at λ=633nm are included in this library. These components are the building blocks of various photonic devices and systems for different applications such as light detection and ranging (Lidar) and chemical or biological sensing. The components in the library have been designed and optimized using finite difference eigenmode (FDE) and finite difference time domain (FDTD) solvers. The components of this library were fabricated using applied nanotools (ANT) SiN multi-project wafer (MPW) run. In this MPW run electron beam lithography is used for waveguide patterning. The minimum feature size is 120 nm and the minimum feature spacing is 120 nm. Fully-etched devices are created using anisotropic inductively coupled plasma - reactive ion etching (ICP-RIE) process. The components were experimentally characterized and measurement results were obtained.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".