Design and Optimization of Surface-Wave Enhanced Reflecting Metasurfaces Featuring Beamforming and Polarization Control
Bibliographic record
Abstract
Electromagnetic metasurfaces (MTSs) are typically modeled as passive, lossless, and purely reactive homogenized impedance sheets on dielectric substrates. In this paper, we present an overview of designing reflecting MTSs using the method of moments (MoM) with gradient-based optimization. The proposed MTSs consist of printed metallic patterns placed over a grounded dielectric slab, with the patterns modeled by impedance sheets, and the impedance values optimized through gradient-descent to achieve specific far-field (FF) power patterns or beamforming objectives. This beamforming/beamshaping capability is enabled utilizing auxiliary surface waves, without the need of any explicit gain or loss. Full-wave simulations of realistic copper structures are presented to validate the proposed design procedure, with measurements preformed for an one dimensional (1D) MTS. An example of a 2D MTS featuring polarization control is also presented.
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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) | 0.001 | 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 | 0.001 | 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".