All‐Dielectric Huygens’ Meta‐Waveguides for Resonant Integrated Photonics
Bibliographic record
Abstract
Abstract The growing maturity of nanofabrication technology has recently enabled the deployment of high‐quality subwavelength nanostructures on photonic chips. Combining existing photonic waveguide technology with the paradigms adapted from metamaterials opens new avenues towards unprecedented control of guided light waves. However, developing new functionalities while preserving efficiencies and offering compatibility with current technology remains a major challenge in on‐chip nanophotonics. Here, a novel silicon nanophotonic waveguide comprising a chain of resonantly forward scattering nanoparticles empowered by spectrally overlapping electric and magnetic dipolar Mie‐type resonances is proposed and demonstrated. The propagation loss of the meta‐waveguides in the telecom spectral range is as low as 0.4 dB mm −1 , exceeding the current record for Mie‐resonant waveguides by more than an order of magnitude. Furthermore, the meta‐waveguides support a negative group index over a broad spectral range of 60 nm and regions of vanishing and anomalous dispersion within the transmission band. Finally, it is shown that meta‐waveguide topologies can implement compact resonance‐protected waveguide bends and efficient splitters within just 320 nm propagation length. This work addresses the fundamental challenges of miniaturization, dispersion, and scattering control in integrated photonics and opens new opportunities for enhancing light–matter interactions, interfacing nanophotonic components, and developing nonlinear, ultrafast, and quantum optics resonant on‐chip 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 | 0.001 |
| 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".