Micromechanical resonators and filters for microelectromechanical system applications
Bibliographic record
Abstract
Microelectromechanical system (MEMS) based mechanical resonators and filters have shown promising characteristics in achieving high Q values and good stability. In this article, new designs for integrated circuit–compatible microelectromechanical intermediate frequency (IF) filters are introduced. These filters have been fabricated and tested, and experimental results are included in this work. One of the novel filters is composed of two-cantilever beam resonators coupled by a soft flexural-mode beam. Different configurations of these filters and their (RF) simulation and experimental responses are presented. One of the advantages of these filters is that the coupling elements can be added from more than one side in order to have elliptical responses. The authors also introduce a novel V-shape coupling element that is used to mechanically couple two clamped-clamped MEMS resonators laterally. The stiffness of the proposed V-shape coupling element is adjustable via changing the length of the V sidelines and/or the V conjunction angle to flatten the filter passband. A two-pole bandpass filter operating in the IF range is constructed using these coupling elements. A lumped modeling approach is presented for a fast and accurate filter design and optimization. Using finite element analysis, the validity and accuracy of the lumped model are investigated. The fabricated filters have center frequencies varying from 700kHzto1.7MHz, with quality factors of 300–1500 when tested at ambient pressure. The experimental results are presented and compared with lumped and finite element simulation results.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".