Enhanced second-harmonic generation in high-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Q</mml:mi></mml:math> all-dielectric metasurfaces with backward frequency conversion
Bibliographic record
Abstract
Here we employ the quasibound state in the continuum (quasi-BIC) resonance in all-dielectric metasurfaces for efficient nonlinear processes in consideration of the backward frequency conversion. We theoretically study second-harmonic generation (SHG) from symmetry-broken AlGaAs metasurfaces and reveal the efficiency enhancement empowered by high-$Q$ quasi-BIC resonances. By introducing the correction term of nonlinear polarization at the fundamental wave field to the conventional undepleted approximation, we uncover the effect of backward frequency conversion on the nonlinear conversation efficiency. The SHG efficiency of $2.45\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}2}$ with the developed depleted model shows a $14.3%$ decrease compared with $2.86\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}2}$ in the conventional undepleted approximation, under an incident intensity of $10\phantom{\rule{4pt}{0ex}}\mathrm{MW}/{\mathrm{cm}}^{2}$. Our results are of importance for designing efficient nonlinear metasurfaces supporting high-$Q$ resonances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".