Highly efficient ultra-broad beam silicon nanophotonic antenna based on near-field phase engineering
Bibliographic record
Abstract
Optical antennas are a fundamental element in optical phased arrays (OPA) and free-space optical interconnects. An outstanding challenge in optical antenna design lies in achieving high radiation efficiency, ultra-compact footprint and broad radiation angle simultaneously, as required for dense 2D OPAs with a broad steering range. Here, we demonstrate a fundamentally new concept of a nanophotonic antenna based on near-field phase-engineering. By introducing a specific near-field phase factor in the Fraunhofer transformation, the far-field beam is widened beyond the diffraction limit for a given aperture size. We use transversally interleaved subwavelength grating nanostructures to control the near-field phase. A Bragg reflector is used at the end of the grating to increase both the efficiency and the far-field beam width. The antenna has a compact footprint of 3.1 µm × 1.75 µm and an ultra-broad far-field beam width of 52° and 62° in the longitudinal and transversal direction, respectively, while the radiation efficiency reaches 82% after incorporating a bottom reflector to further improve the directionality. This unprecedented design performance is achieved with a single-etch grating nanostructure in a 300-nm SOI platform.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".