Plasmonic meta-screen for alleviating the trade-offs in the near-field optics
Bibliographic record
Abstract
We propose a "meta-screen" design, consisting of a metallic sheet patterned with a dense array of nano-sized slot antennas, for inducing sub-wavelength optical spots in the near-field. Compared to other transmission screen topologies, our design overcomes the trade-off of low throughput versus resolution of a sub-wavelength aperture by inducing resonance in the slots. In addition, the antenna array serves to effectively narrow the spot size through the superposition of spatially shifted beams produced by each slot element. Such a design offers a practical approach for extending the near-field sensing/imaging distance at optical frequencies. The effectiveness of narrowing the spot size through the array topology is demonstrated by evaluating the full-width-half-maximum (FWHM) beamwidth at a distance of 0.1lambda(0) away from the screen. We show that an array with just three elements improves the beamwidth by more than 30% compared to a single resonant slot element. In this paper, important issues such as the operating principle and the design process of the meta-screen, the characteristics of plasmonic slot antenna, the impact of the number of array elements, and the effect of asymmetry due to the presence of a supporting substrate are discussed.
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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 | 0.001 |
| 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".