Optimization of a programmable <i>λ</i> /2‐pitch optical phased array
Bibliographic record
Abstract
A challenge in optical phased arrays (OPAs) is to achieve single-lobe emission using densely spaced emitters without incurring inter-waveguide optical crosstalk. Here, we propose to heuristically optimize the amplitude and phase of each grating antenna in an OPA to correct for optical non-idealities, including fabrication variations and inter-waveguide crosstalk. This method was applied to a silicon photonic integrated circuit with 1 mm-long gratings at 775 nm spacing for operation in a wavelength range of 1450-1650 nm. We achieved a wide two-dimensional beam-steering range of 110° × 28°, evaluated over a 127° × 47° field-of-view (FOV). Within this FOV, we measured an average sidelobe suppression of 8.2 dB and focused on average, 34.5 % of the emitted power into the main lobe. We achieved a peak sidelobe suppression of 14.5 dB and 50 % of the power concentrated in the main lobe. The approach is suitable for applications that require alias-free out-of-plane emission.
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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) | 0.001 | 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".