Theory and Design of Dual-Band Microstrip Networks Using Embedded Metamaterial-Based Electromagnetic Bandgap Structures
Bibliographic record
Abstract
This article examines implementations of the recently introduced metamaterial-based electromagnetic bandgap (MTM-EBG) structure, which may be embedded directly into the transmission-line (TL) segments of microstrip (MS) networks and whose dispersion properties may be accurately determined using multiconductor TL (MTL) theory. Two methods of imparting dual-band behavior to MS-based networks are introduced: utilizing stopband and passband regions of the EBG for frequency-selective signal routing, and using the phasing properties of the EBG to provide equal phase shifts at two separate frequencies. Two topological variants of the MTM-EBG are introduced and shown to provide different bandgap sizes, suiting each proposed application. A dual-band Wilkinson power divider, a dual-band quadrature hybrid coupler, and a dual-band impedance transformer are designed with embedded MTM-EBGs, which due to their ability to be realized in an entirely uniplanar form without vias and using a single dielectric layer, allow these devices to be entirely printable as well. The performance characteristics of all three devices are simulated and demonstrate excellent agreement with measurements. The design methodology is general and based on a rigorous MTL circuit model, making this approach amenable to a variety of MS networks, beyond what is 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".