Complex 10-nm resolution nanogap and nanowire geometries for plasmonic metasurface miniaturization
Bibliographic record
Abstract
Emerging electromagnetic inverse design methods have pushed nanofabrication methods to their limits to extract maximum performance from plasmonic aperture-based metasurfaces. Using plasmonic metamaterial-lined apertures as an example, we demonstrate the importance of fine nanowire and nanogap features for achieving strong miniaturization of plasmonic nanoapertures. Metamaterial-lined nanoapertures are miniaturized over bowtie nanoapertures with identical minimum feature sizes by a factor of 25% without loss of field enhancement. We show that features as small as 10 nm can be reliably patterned over the wide areas required of metasurfaces using the helium focused ion beam microscope. Under imperfect fabrication conditions, we achieve 11-nm-wide nanogaps and 12-nm-wide nanowires over an area of 13 µm 2 , and successfully validate our results with optical characterization and comparable full-wave simulations.
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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".