A Technique for Designing Multilayer Multistopband Frequency Selective Surfaces
Bibliographic record
Abstract
A systematic technique for designing and optimizing multilayer frequency selective surfaces (FSSs) with low overall profile is presented. Periodic scatterers in the shape of loaded dipoles (dogbones) are used on each layer to create a single-stopband response. Multiple such layers are cascaded together to create the desired multistopband response. An equivalent circuit model for a multilayer FSS that explicitly and intuitively accounts for electromagnetic coupling interactions between the layers is proposed and investigated. This model is used in a novel design method, which precompensates for the effect of coupling during circuit-based design stage rather than postcompensating through iterative full-wave (FW) optimization after the design stage, as in most traditional approaches. As a consequence, this approach has the potential to greatly speed up the design process by enabling considerable simplifications during FW simulations. The proposed method is used to design several ultralow-profile triple-layer, triple-stopband surfaces intended for Wi-Fi applications. The interlayer spacing is as low as λ/75 at the highest operating band (5.2 GHz), making the overall thickness extremely small. The unit cell size for the designs is about λ/5 at 5.2 GHz. The designs are fabricated and tested to validate the proposed methodology.
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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 | 0.000 | 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".