Pull-In Extension of MEMS Electrostatic Microactuators Using an Active Control Method
Bibliographic record
Abstract
Parallel-plate and transverse comb-drive types of electrostatic microactuators are commonly used MEMS-based devices. Although they have the advantages of favorable scaling, fast response, and low power consumption, these electrostatic microactuators have had a fundamental limitation in that the allowable travel range is limited to 1/3 of the total gap between comb capacitor plates. Travel beyond this allowable range results in “pull-in” instability, independent of mechanical design parameters such as stiffness and mass. This paper presents the extension of stable travel ranges through the development of an active control system that stabilizes electrostatic microactuators and allows travel almost over the entire available gap between comb capacitor plates, providing a practical approach to extending travel range of electrostatic microactuators for applications that require high fill factors. The addressed challenges include the nonlinear dynamics of microactuators and system parameters that vary among fabricated devices. A nonlinear model inversion technique was proposed to address the nonlinear dynamics, which allows the use of traditional linear controller design methodologies for obtaining a desired linear system response. An adaptive controller was developed to provide improved position tracking in the presence of device parameter variations caused by fabrication imperfections. For experimental verification, the control system was implemented on a transverse comb-drive electrostatic microactuator fabricated using deep reactive ion etching on silicon-on-insulator wafers. Experimental results demonstrate that the resulting system is capable of traveling 4.0μm over a 4.5μm full range without “pull in.” Satisfactory tracking performance was obtained over a wide frequency band.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".