Characterization of a Tunable MEMS RF Filter
Bibliographic record
Abstract
We present a reduced-order analytical model to describe the response of a tunable MEMS RF filter to an input signal whose frequency is in the neighborhood of the passband. It extends our earlier model by allowing for the application of independent DC voltages in addition to an AC input signal. The model is obtained by discretizing the distributed-parameter system using a Galerkin procedure. It consists of two second-order nonlinearly coupled ordinary-differential equations. Using the method of multiple scales, we determine four first-order nonlinear ordinary-differential equations describing the amplitudes and phases of the modes. We found that mismatch between the natural frequencies of the resonators modifies the global modes significantly, leading to localization of the response in either the input or the output beam. We found that the filter can be tuned to operate linearly for a wide range of VAC by choosing a DC voltage that makes the effective nonlinearities vanish. Amplifying the input signal VAC to improve the filter performance creates multi-valued responses beyond a threshold in the case of non-zero effective nonlinearities.
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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 | 0.000 |
| Open science | 0.000 | 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".