Novel spot-size converter for broadband and polarization insensitive coupling to conventional single-mode fiber
Bibliographic record
Abstract
In optical integrated circuits (OICs), inverted tapered spot-size converters (SSC) provide a high coupling efficiency between a silicon nanowire and an optical fiber. However, to reduce the packaging costs of these OICs, it is beneficial to use a SSC with a mode field diameter that matches to that of a conventional single-mode fiber (SMF). Such a SSC offers high alignment tolerance for butt-coupling with a SMF, without the need for a specialized lensed or tapered fiber. In this work, we propose a novel SSC which is not only broadband but also polarization insensitive. The proposed SSC is composed of a stack of Si3N4/SiO2 layers deposited on top of a silicon nanowire. The most optimal modal overlap of our SSC with a conventional SMF of radius 4.2 μm showed 94% and 99%, for TE and TM polarization, respectively. This multilayer stack is tapered along the propagation direction to transfer power to a tapered silicon nanowire. We have studied the adiabatic transfer of power by optimizing the taper lengths such that the minimum loss can be achieved for both polarizations. Our design demonstrates a record low overall coupling loss (including losses due to mode overlap, tapered design, and reflection) of 0.71 dB (for TE) and 0.48 dB (for TM) between a conventional SMF and the silicon nanowire at the wavelength of 1.55 μm. The variation in coupling loss due to the tapered design is less than 0.6 dB over the wavelength range from 1.5 μm to 1.6 μm.
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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) | 0.001 | 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".