High-$Q$ Tunable Dielectric Resonator Filters Using MEMS Technology
Bibliographic record
Abstract
This paper presents the design and implementation of a new class of high-Q tunable dielectric resonator (DR) filters based on microelectromechanical systems (MEMS) technology. The use of MEMS tuning elements results in the compact implementation of the proposed filters with high-Q and near to zero dc power consumption. The proposed filters consist of disk-shaped dielectric resonators with circular holes created in the center of each resonator. Three different filters are designed and measured based on different tuning elements. The first filter operates in TME mode at a center frequency of 4.72 GHz with a bandwidth of 21 MHz. MEMS contact-type switches are used as tuning elements for this filter. Measurement results demonstrate a tuning range of 160 MHz while the quality factor is above 510 (1200-510 over the tuning range). The other two implementations employ GaAs and MEMS varactors for tuning. The tunable filter with GaAs varactor has a continuous tuning range from 4.97 to 4.87 GHz with 65-MHz bandwidth and a Q value from 660 to 170. The MEMS varactor-tuned filter has a better tuning performance from 5.20 to 5.02 GHz with higher Q value from 800 to 550 over the tuning range. The proposed tuning approach is applicable to other modes at other frequencies of DR filters.
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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".