Effect of Beam-Steering Angle, Operation Wavelength and Mean Inter-Element Distance on the Side-Lobe Levels of Integrated Optical Phased Arrays under Beam-Steering Operation
Bibliographic record
Abstract
Integrated optical phased arrays (OPAs) play an important role in a broad range of applications. Fabrication constraints, however, pose a limit to the minimum inter-element separation that further results in high-intensity side lobes. The intensity of these secondary lobes can be reduced by arranging the antenna elements with non-uniform separation distance, which has been addressed by different methods. In this paper we employ one of the already proven optimization algorithms, i.e., differential evolution, to optimize the element positions of linear arrays with different configurations operating under beam-steering operation and considering a minimum inter-element distance. These optimizations allowed us to derive some design guidelines that can assist in reducing the side-lobe level (SLL) of integrated linear OPAs. In particular, we found that it is necessary to optimize the positions for the broadest beam-steering angle and the shortest operation wavelength. Additionally, optimizations of different configurations reveal that, when imposing a minimum inter-element distance, there is an optimum mean distance that minimizes the SLL of the array.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".