Novel spot size converter for coupling standard single mode fibers to SOI waveguides
Bibliographic record
Abstract
We have designed and numerically simulated a novel spot size converter for coupling standard single mode fibers with 10.4μm mode field diameter to 500nm × 220nm SOI waveguides. Simulations based on the eigenmode expansion method show a coupling loss of 0.4dB at 1550nm for the TE mode at perfect alignment. The alignment tolerance on the plane normal to the fiber axis is evaluated at ±2.2μm for ≤1dB excess loss, which is comparable to the alignment tolerance between two butt-coupled standard single mode fibers. The converter is based on a cross-like arrangement of SiOxNy waveguides immersed in a 12μm-thick SiO2 cladding region deposited on top of the SOI chip. The waveguides are designed to collectively support a single degenerate mode for TE and TM polarizations. This guided mode features a large overlap to the LP01 mode of standard telecom fibers. Along the spot size converter length (450μm), the mode is first gradually confined in a single SiOxNy waveguide by tapering its width. Then, the mode is adiabatically coupled to a SOI waveguide underneath the structure through a SOI inverted taper. The shapes of SiOxNy and SOI tapers are optimized to minimize coupling loss and structure length, and to ensure adiabatic mode evolution along the structure, thus improving the design robustness to fabrication process errors. A tolerance analysis based on conservative microfabrication capabilities suggests that coupling loss penalty from fabrication errors can be maintained below 0.3dB. The proposed spot size converter is fully compliant to industry standard microfabrication processes available at INO.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".