Efficient Integrated Graphene Photonics in the Visible and Near‐IR
Bibliographic record
Abstract
Abstract Graphene photonics has emerged as a promising platform for providing desirable optical functionality. However, graphene's monolayer‐scale thickness fundamentally restricts the available light matter interaction, posing a critical design challenge for integrated devices, particularly in wavelength regimes where graphene plasmonics is untenable. While several plasmonic designs have been proposed to enhance graphene light interaction in these regimes, they suffer from substantial insertion loss due to metal absorption. Here we report a non‐resonant metamaterial‐based waveguide platform to overcome the design bottleneck associated with graphene device. Such metamaterial structure enables low insertion loss even though metal is being utilized. By examining waveguide dispersion characteristics via closed‐form analysis, it is demonstrated that the metamaterial approach can provide optimized optical field that overlaps with the graphene monolayer. This enables graphene‐based integrated components with superior optical performance. Specifically, the metamaterial‐assisted graphene modulator can provide 5‐fold improvement in extinction ratio compared to Si nanowire, while reducing insertion loss by one order magnitude compared to plasmonic structures. Such a waveguide configuration thus allows one to maximize the optical potential that graphene holds in the telecom and visible regimes.
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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) | 0.001 | 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".