Novel Linearly Tunable MEMS Capacitors With Flexible Moving Electrodes
Bibliographic record
Abstract
In conventional MEMS parallel-plate capacitor designs, the moving electrode is commonly modeled as a rigid plate with flexible boundary conditions provided by a set of supporting beams. Such a capacitor generates limited tuning ratio up to 1.5 and its capacitance-voltage response is nonlinear. This paper presents novel designs where the moving electrodes are fixed-edge flexible plates. The plate displacement is selectively limited by a set of rigid steps, located between two electrodes, to generate a smooth and linear response and high tunability. Three different step heights are considered in the design and the linearity of the C-V curve is maximized by modifying the geometry of the plate, and changing the location and order of steps. Since the analytical solution for coupled electrostatic-structural physics in this case does not exist, ANSYS® FEM simulation is performed to obtain the C-V curves and optimize the design. Two designs with different electrode shapes, rectangular and circular, are developed. For rectangular-plate capacitors, tunabilities ranging from 120% to 140% and high linearity are achieved. Circular-plate designs, on the other hand, generate lower tunabilities and an extremely linear region in C-V curves. Design methodology introduced in this research is not limited to proposed geometries and can be extended to different topologies to obtain a combination of high tunability and linearity.
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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".