Large-Angle, Broadband and Multifunctional Gratings Based on Directively Radiating Waveguide Scatterers
Bibliographic record
Abstract
Conventional surface-relief gratings are inefficient at deflecting normally-incident light by large angles. This constrains their use in many applications and limits the overall efficiency of any optical instrument integrating gratings. Here, we demonstrate a simple approach for the design of diffraction gratings that can be highly efficient for large deflection angles, while also offering additional functionality. The gratings are composed of a unit cell comprising a vertically-oriented asymmetric slot-waveguide. The unit cell shows oscillating unidirectional scattering behavior that can be precisely tuned as a function of the waveguide length. This occurs due to interference between multiple modes excited by the incident light. In contrast to metasurface-based gratings with multiple resonant sub-elements, a periodic arrangement of such non-resonant diffracting elements allows for broadband operation and a strong tolerance for variations in angle of incidence. Full-wave simulations show that our grating designs can exhibit diffraction efficiencies ranging from 94% for a deflection angle of 47$^\circ$ to 80% for deflection angle of 80$^\circ$. To demonstrate the multifunctionality of our grating design technique, we have also proposed a flat polarization beamsplitter, which allows for the separation of the two orthogonal polarizations by 80$^\circ$, with an efficiency of 80%.
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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".