The MTM-EBG as a Rigorous Multiconductor Model of the UC-EBG and Approaches for Miniaturization
Bibliographic record
Abstract
A multiconductor transmission-line (MTL) model is proposed with which to model the uniplanar compact electromagnetic bandgap structure (UC-EBG). The model is based on a 2-D arrangement of metamaterial-based electromagnetic bandgap structures (MTM-EBGs), with a central node region. The branches of this structure are modeled as conductor-backed coplanar waveguide segments with integrated series capacitors, while the node region is modeled as a grid of microstrip (MS) lines with suitably augmented propagation velocities. The dispersion properties of the canonical UC-EBG unit cell are simulated in Ansys HFSS and compared with those of an equivalent simulation model based on a 2-D arrangement of weakly loaded MTM-EBGs, as well as similar data produced by the MTL equivalent-circuit model, which are all found to be in agreement. It is then shown that additional capacitive loading allows for substantial miniaturization, a regime in which even better agreement is produced between the equivalent circuit model’s dispersion data and the data produced by an equivalent HFSS model. Using these data, the Bloch modes of this structure are analyzed in detail. Thus, the MTL perspective offers a recipe for producing highly miniaturized UC-EBG unit cells that may be successfully integrated into printed circuit board (PCB) systems for applications in parallel-plate noise and surface-wave (SW) mode suppression, among others.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".