Substrate Integrated Waveguide Filter With Wide Stopband Up to (2<i>k</i> + 3) <i>f</i> <sub>0</sub>
Bibliographic record
Abstract
With the development of multistandard, multiband wireless/microwave circuits and systems, a wide stopband could be essential for a substrate integrated waveguide (SIW) filter to eliminate interference. However, the performance of the current wide-stopband SIW filters is not good enough, particularly the stopband extension. Here, based on a multilayer SIW filter without degrading its passband performance, we advance the slot element into a slot array as the intercoupling structure to significantly improve the stopband extension upper limit. It is achieved by splitting the conventional magnetic intercoupling slot element into a slot array with$2k$parts and moving them away from the edge by about 0.5/($2k$+ 1) of the edge’s length. For an SIW filter working in TE101 ($f_{0}$), one can use Type-0,$\text {Type-1}, \ldots $, and Type-$k$slot arrays to eliminate all the spurious modes below${\mathrm {TE}}_{(2k + 3)0(2k+3)}$and extend the stopband to ($2k$+ 3)$f_{0}$. In this paper, we present three prototypes that respectively use 2–4 types of slot arrays. Without complex design or passband degradation, the measured results show that their stopbands are, respectively, extended up to$4.67f_{0}$,$7.03f_{0}$, and$9.31f_{0}$, which are significantly better than those of their counterparts based on the slot element. The proposed technology should be efficient for developing high-performance wide-stopband SIW filters in wireless/microwave circuits and systems.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".