Integrated Photonic Functions Using Anisotropic 2D Material Structures
Bibliographic record
Abstract
Plasmonic waveguides based on 2D materials, which enable the formations of guided modes confined around few-layered material, are promising plasmonic platforms for the miniaturization of photonic devices. Nonetheless, such waveguides support modes that are evanescent in the waveguide core with the majority of the fields concentrated around waveguide edges, which are different from those supported by 3D dielectric waveguides where the modal fields are of oscillatory nature and peak at the center. As a result, many photonic devices and functionalities that can be achieved within 3D dielectric waveguides based on total-internal-reflation modes cannot be realized using 2D material-based plasmonic structures. In this work, we propose and demonstrate how to leverage anisotropy in 2D materials to tailor of modal fields supported by 2D material waveguide for the first time. By regulating material absorption of the constituent 2D materials, the modal fields of these 2D modes can be tailored to localize around the waveguide center, which in turn can improve the efficiencies of coupling-based photonic functions using 2D materials, from in-plane multimode-interference couplers to out-of-plane optical radiation. Using natural anisotropic 2D materials such as black phosphorus, these pivotal functions can expand existing device capabilities that are typically achieved in 3D dielectrics but using 2D materials, thus allowing for the implementation of 2D plasmonic circuits with no need to relying on 3D layers.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".