Experimental demonstration of a silicon nanophotonic antenna for far-field broadened optical phased arrays
Bibliographic record
Abstract
Optical antennas play a pivotal role in interfacing integrated photonic circuits with free-space systems. Designing antennas for optical phased arrays ideally requires achieving compact antenna apertures, wide radiation angles, and high radiation efficiency all at once, which presents a significant challenge. Here, we experimentally demonstrate a novel ultra-compact silicon grating antenna, utilizing subwavelength grating nanostructures arranged in a transversally interleaved topology to control the antenna radiation pattern. Through near-field phase engineering, we increase the antenna’s far-field beam width beyond the Fraunhofer limit for a given aperture size. The antenna incorporates a single-etch grating and a Bragg reflector implemented on a 300-nm-thick silicon-on-insulator (SOI) platform. Experimental characterizations demonstrate a beam width of 44°×52° with −3.22 dB diffraction efficiency, for an aperture size of 3.4 μm ×1.78 μm . Furthermore, to the best of our knowledge, a novel topology of a 2D antenna array is demonstrated for the first time, leveraging evanescently coupled architecture to yield a very compact antenna array. We validated the functionality of our antenna design through its integration into this new 2D array topology. Specifically, we demonstrate a small proof-of-concept two-dimensional optical phased array with 2×4 elements and a wide beam steering range of 19.3º × 39.7º. A path towards scalability and larger-scale integration is also demonstrated on the antenna array of 8×20 elements with a transverse beam steering of 31.4º.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".