Multiband Impedance Matching Using Microstrip-Embedded MTM-EBGs
Bibliographic record
Abstract
This work investigates a general method of multiband impedance matching using a double-stub tuner (DST) loaded with metamaterial-based electromagnetic bandgap structures (MTM-EBGs). MTM-EBGs are uniplanar and fully printable and can be embedded directly into microstrip (MS) stubs to imbue them with designable electrical lengths at two or more harmonically unrelated frequencies, without increasing the stub footprint. Combinations of these stubs, inspired by the DST, can produce multiband impedance matching to arbitrary, complex, and frequency-dependent loads. A dual-band matching network is first presented with this method, and a general design procedure is developed to match at two desired frequencies while maximizing bandwidth. Matching bandwidths of 44.4% and 22.1% around 2.4 and 5.8 GHz are observed. Next, a tri-band matching network is designed and fabricated for operation at 2.4/3.6/5.8 GHz, again following a general and well-established design procedure and presenting bandwidths of 16.8%/6.6%/18.3% around the three operating frequencies, respectively. The resulting circuits are uniplanar and compact, requiring a similar footprint to that of a single-band DST, and the matching networks are designed to ensure that dissipative losses in the matched bands are minimized. In all cases, measurement results of the uniplanar devices show excellent agreement with simulations.
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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 | 0.001 |
| 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".