A Novel Linear MEMS Capacitor With Triangular Electrodes and Nonlinear Structural Stiffness
Bibliographic record
Abstract
MEMS parallel-plate tunable capacitors have high Q-factors and fast responses to the actuation and therefore are desired for RF applications. However, conventional designs have low tuning ratios and nonlinear capacitance-voltage (C-V) responses which are highly sensitive to the voltage change near pull-in. In this research, a novel structure for parallel-plate-based capacitors is introduced. The capacitor has electrodes with triangular shape and uneven supporting beams and is equipped with a set of middle beams which increases the structural stiffness of the capacitor as bias voltage increases. Because the asymmetric design alters the parallelness of the plates, the stiffness of each middle beam is added to the system at a different voltage causing a smooth increment in structural stiffness. To analyze the capacitor and optimize the design, an analytical model is developed to solve the coupled electrostatic-structural physics. The results of numerical simulations reveal that if the stiffness coefficients of supporting and middle beams are optimized, a highly linear C-V response is obtained. Moreover, since the structural rigidity is gradually increased with voltage, the sensitivity of the response to the voltage change is also improved and a higher tunability over 150% is achieved. The proposed design has a simple geometry and can be fabricated by a three-structural-layer process such as PolyMUMPs.
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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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".