Modeling graphene based surface plasmon waveguides and devices
Bibliographic record
Abstract
Graphene is different from most optical materials in that it is a thin material layer with a thickness as small as one atom. Graphene layers can be incorporated into optical simulations using either a surface conductivity material model or a volumetric permittivity material model; however, introducing graphene through a volumetric permittivity is computationally inefficient because it requires very fine discretization grids. We have recently developed a more efficient approach that enables the use of comparatively coarse grids by formulating a discretization of Maxwell’s equations (in the time or frequency domains) that combines a surface conductivity description of graphene layers with a volumetric permittivity description of other optical materials. This approach includes the full dispersion characteristics of graphene as specified by the Kubo formula. This paper demonstrates how the combined material description approach can be used to efficiently model state-of-the-art devices that take advantage of the energy confinement provided by surface plasmons. We show how to efficiently model TE and TM polarized surface plasmons, a surface plasmon waveguide switch, and an electro-optical modulator. This last example also includes electrical simulations of graphene and demonstrates how both optical and electrical simulations can be combined to produce a complete model of a graphene based device. For each example, we compare with previously published results, including experimental results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".