Scaling down photonic waveguide devices on the SOI platform
Bibliographic record
Abstract
We discuss the challenges encountered when scaling down photonic waveguide devices, and demonstrate possible solutions in silicon-on-insulator (SOI) platform. First, sources of waveguide birefringence such as waveguide geometry and stress in the waveguiding layer are discussed. Birefringence sensitivity to inaccuracy of waveguide dimensions is compared for different waveguide geometries, including trapezoidal and rectangular cross-sections. Results show that trapezoidal waveguides are more robust, which makes fabrication tolerances less stringent. Methods for minimizing the waveguide birefringence using stress induced by an over-cladding dielectric film, and by inducing form birefringence through deposition of thin layers of high and low refractive index materials, are discussed. Compact arrayed waveguide grating (AWG) devices are presented, with internal loss of -5.9 dB, crosstalk better than -20 dB, and polarization dependent wavelength shift of <0.05 nm. We discuss and quantify the sources of loss and crosstalk in our AWG devices, and review the methods we have developed for compensation of the polarization dependent wavelength shift, including etched compensator and silicon-oxide-silicon (SOS) compensator. The latter exploits form birefringence of a thin buried oxide layer sandwiched between silicon waveguide core and a silicon over-layer, and is simple to fabricate by standard oxide and amorphous silicon or polysilicon deposition techniques. The calculated loss penalty of the SOS compensator is less than 0.2 dB for both TE and TM polarization.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".