Diffraction-less propagation beyond the sub-wavelength regime: a new type of nanophotonic waveguide
Bibliographic record
Abstract
Sub-wavelength grating (SWG) metamaterials have garnered a great interest for their singular capability to shape the propagation of light. However, practical SWG implementations are limited by fabrication constraints, such as minimum feature size. Here, we present a new nanophotonic waveguide grating concept that exploits phase-matching engineering to suppress diffraction effects for a period three times larger than those with SWG approaches. This long-period grating not only facilitates fabrication, but also enables a new diffraction-less regime with additional degrees of freedom to control light propagation. More specifically, the proposed phase-matching engineering enables selective diffraction suppression, providing new tools to shape propagation in the grating. We harness this flexible diffraction control to yield single-mode propagation in, otherwise, highly multimode waveguides, and to implement Bragg filters that combine highly-diffractive and diffraction-less regions to dramatically increase light rejection. Capitalizing on this new concept, we experimentally demonstrate a Si membrane Bragg filter with record rejection value exceeding 60 dB. These results demonstrate the potential of the proposed long-period grating for the engineering of diffraction in nanophotonic waveguides and pave the way for the development of a new generation of high-performance Si photonics devices.
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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 | 0.001 |
| 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".