Micromachined Capacitive Sensor With Configured Boundaries: Approach, Design and Fabrication
Bibliographic record
Abstract
A new set of capacitive-based resonators is introduced in this work that utilizes a unique configured boundaries approach. The proposed design strategy is utilized to develop and characterize three circular resonators as proof-of-concept with strategically configured boundaries. For a fair comparison, the introduced novel resonators along with a reference conventional structure are fabricated using a commercially available fabrication technique, PolyMUMPs. The effects of the presented approach on plate deflection and electromechanical coupling coefficient are demonstrated through finite element analysis (FEA) using COMSOL Multiphysics as well as experimental characterizations. The fabricated devices are electrically evaluated. The white light interferometry is further used for deflection measurements. The device performance evaluations illustrate a robust control of the resonator’s plate stiffness and deflection while maintaining the same dimensions. Experimental evaluations demonstrate an enhanced deflection profile of the plate, which significantly increases the average deflection by up to 50% compared to the conventional resonator developed with the same dimensions and fabrication process. Furthermore, it is observed that using the proposed approach reduces the pull-in voltage by up to 17% compared to the conventional resonators while preserving the resonant frequency within ±150 kHz.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".