A High-Gain Broadband Gradient Refractive Index Metasurface Lens Antenna
Bibliographic record
Abstract
The design, simulation, and measurement results of a high-gain broadband gradient refractive index (GRIN) planar lens fed by an antipodal exponential taper slot antenna (ATSA) are presented. As a constituent part of this lens, a novel nonresonant metamaterial unit cell, composed of bilayer triple rectangular rings, is proposed and its equivalent circuit model is developed and described. It is shown that, by utilizing this element, stronger capacitive couplings between adjacent metallic layers are realized resulting in a large refractive index variation of about 2.5, and hence, a thin lens with a thickness of 0.38λ0, where λ0is the wavelength at 9.5 GHz. In addition, since the unit cell is designed to resonate at higher frequencies, its refractive index response is smoothly increased over a broad frequency range and this considerably enhances the operating bandwidth of the lens. The achieved measured results demonstrate a broad matching and -3 dB gain bandwidths of 52% (7-12 GHz) and 65% (7-13.2 GHz), respectively. Furthermore, this lens offers a high aperture efficiency of 50% (21.2 dB gain) at the center frequency, and its sidelobe and cross-polarization levels are less than -20 dB and -26 dB across the entire matched band, respectively.
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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".