Circular Optical Phased Arrays with Radial Nano-Antennas
Bibliographic record
Abstract
On-chip optical phased arrays (OPAs) are the enabling technology for diverse applications, ranging from optical interconnects to metrology and light detection and ranging (LIDAR). To meet the required performance demands, OPAs need to achieve a narrow beam width and wide-angle steering, along with efficient sidelobe suppression. A typical OPA configuration consists of either one-dimensional (1D) linear or two-dimensional (2D) rectangular arrays. However, the presence of grating sidelobes from these array configurations in the far-field pattern limits the aliasing-free beam steering, when the antenna element spacing is larger than half of a wavelength. In this work, we provide numerical analysis for 2D circular OPAs with radially arranged nano-antennas. The circular array geometry is shown to effectively suppress the grating lobes, expand the range for beam steering and obtain narrower beamwidths, while increasing element spacing to about 10 μm. To allow for high coupling efficiency, we propose the use of a central circular grating coupler to feed the designed circular OPA. Leveraging radially positioned nano-antennas and an efficient central grating coupler, our design can yield an aliasing-free azimuthal field of view (FOV) of 360°, while the elevation angle FOV is limited by the far-field beamwidth of the nano-antenna element and its array arrangement. With a main-to-sidelobe contrast ratio of 10 dB, a 110-element OPA offers an elevation FOV of 5° and an angular beamwidth of 1.14°, while an 870-element array provides an elevation FOV up to 20° with an angular beamwidth of 0.35°. Our analysis suggests that the performance of the circular OPAs can be further improved by integrating more elements, achieving larger aliasing-free FOV and narrower beamwidths. Our proposed design paves a new way for the development of on-chip OPAs with large 2D beam steering and high resolutions in communications and LIDAR systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".