Engineering Local Fields in Nonlinear Plasmonic Metasurfaces -INVITED
Bibliographic record
Abstract
Nonlinear optical phenomena are paramount in many photonic applications ranging from frequency broadening and generation of ultrashort pulses to frequency comb-based metrology. A recent trend has been to miniaturize photonic components, resulting also in a demand for small scale nonlinear components. This demand is difficult to address by using conventional materials motivating the search for alternative approaches. Nonlinear plasmonic metasurface cavities have recently emerged as a promising platform to enable nanoscale nonlinear optics. Despite steady progress, the so far achieved conversion efficiencies have not yet rivalled conventional materials. Here, we discuss our recent work to develop more efficient nonlinear metamaterials, focusing on plasmonic metasurfaces supporting collective responses known as surface lattice resonances. These resonances can exhibit very narrow spectral features, showing potential to considerably enhance nonlinear processes via resonant interactions. We demonstrate a plasmonic metasurface operating at the telecommunications C band that exhibits a record-high quality factor close to 2400, demonstrating an order-of-magnitude improvement compared to existing metasurface cavities. Motivated by this experimental demonstration, we also present numerical predictions suggesting that such metasurfaces could soon answer the existing demand for miniaturized and/or flat nonlinear components.
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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) | 0.002 | 0.001 |
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".