Miniaturized Folded Ridged Quarter-Mode Substrate Integrated Waveguide RF MEMS Tunable Bandpass Filter
Bibliographic record
Abstract
This paper presents a miniaturized RF MEMS tunable bandpass filter design developed by application of folded ridged quarter-mode substrate integrated waveguide cavity. Using packaged RF MEMS SP4T chips, a switchable reactive loading is applied to the folded ridged quarter-mode substrate integrated waveguide cavity, tuning the resonance frequency. Overall miniaturizations of 70.3% and 78.8% are achieved for inductively- and capacitively-loaded filters compared to an RF MEMS tunable filter using half-mode substrate integrated waveguide. Here, an analysis of inductive or capacitive loading along with their advantages and disadvantages are discussed. Two different two-pole folded ridged quarter-mode substrate integrated RF MEMS tunable bandpass filters are designed and measured. The first prototype employing inductive loading achieves 4.4% tuning range at a center frequency of 1.713 GHz, insertion loss of 3.10-3.92 dB, and return loss greater than 15 dB for all tuning modes. The second prototype employing capacitive loading is measured with a 35.2% tuning range at a center frequency of 865 MHz, insertion loss of 1.6-4.3 dB, and return loss greater than 10 dB for all tuning modes. The reported performance and extreme miniaturization of folded ridged quarter-mode substrate integrated waveguide cavities shows great promise for their application in tunable filter design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".