Millimeter-long metamaterial surface-emitting antenna in the silicon photonics platform
Bibliographic record
Abstract
Integrated optical antennas are key components for on-chip light detection and ranging technology (LIDAR). In order to achieve a highly collimated far field with reduced beam divergence, antenna lengths on the order of several millimeters are required. In the high-index contrast silicon photonics platform, achieving such long antennas typically demands weakly modulated gratings with lithographic minimum feature sizes below 10 nm. Here, we experimentally demonstrate a new, to the best of our knowledge, strategy to make long antennas in silicon waveguides using a metamaterial subwavelength grating (SWG) waveguide core loaded with a lateral periodic array of radiative elements. The mode field confinement is controlled by the SWG duty cycle, and the delocalized propagating mode overlaps with the periodic perturbations. With this arrangement, weak antenna radiation strength can be achieved while maintaining a minimum feature size as large as 80 nm. Using this strategy, we experimentally demonstrate a 2-millimeter-long, single-etched subwavelength-engineered optical antenna on a conventional 220 nm SOI platform, presenting a measured far-field beam divergence of 0.1° and a wavelength scanning sensitivity of 0.13°/nm.
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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".