Subwavelength engineering in silicon photonics
Bibliographic record
Abstract
Subwavelength engineering in silicon photonic integrated circuits is a powerful design tool that allows one to synthesize an effective photonic medium with adjustable refractive index. This creates a new degree of freedom in photonic circuit design. We present an overview of the fundamental concept and its application to address several important practical challenges in the implementation of silicon photonics as a next generation photonics platform for telecom, datacom and sensing. In particular, we report our results in developing highly efficient and broadband fiber-chip couplers for silicon photonic wire waveguides using subwavelength engineered edge coupling structures. We experimentally demonstrate a coupling efficiency of −0.4 dB and polarization independent operation for a broad spectral range exceeding 100 nm for optical fiber with a core diameter of 3.2 µm. For coupling to standard SMF-28 fiber with 10.4 µm mode field diameter we numerically demonstrate a subwavelength engineered overlayer structure composed of SiO2and Si3N4which exhibits an overall coupling efficiency exceeding 90%. We have also used our subwavelength structure for coupling experiments with a conventional In- GaAsP/InP buried heterostructure laser at λ = 1.3 µm with a measured near field mode size of 2.1 µm×2.8 µm. Peak coupling efficiency is 1.5 dB with 1-dB alignment tolerance of approximately ±1.2 µm horizontally and ±0.8 µm vertically. We further present subwavelength engineered grating couplers fabricated in a single-etch step for the telecom (1.55 µm) and datacom (1.3 µm) wavelengths with efficiencies exceeding −0.5 dB. Further applications that will be discussed include waveguide crossings, microspectrometers, ultra-fast optical switches, athermal waveguides, evanescent field sensors, polarization rotators and colorless interference couplers.
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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 | 0.001 |
| 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".