Electrostatic Torsional Micromirror With Enhanced Tilting Angle Using Active Control Methods
Bibliographic record
Abstract
Electrostatic microelectromechanical systems (MEMS)-based torsional micromirrors are a fundamental building block for many optical network applications, such as optical wavelength-selective switches, configurable optical add-drop multiplexers and optical cross-connects. Although the device architecture, materials and fabrication processes determine the micromirrors' functioning space, one major technical challenge to achieving their full performance potentials is the controllability and stability of the tilting angle. In this paper, an electrostatic micromirror is designed and fabricated using a standard MEMS silicon-on-insulator (SOI) process. Active control approaches including gain scheduling and nonlinear proportional and derivative (PD) control are proposed. Both approaches can improve the performance of the mirror tilting and enhance the robustness of the structures to any stochastic perturbations. Furthermore, the nonlinear PD control can eliminate the micromirror “pull-in” phenomenon, hence significantly expanding the mirror tilt range, and as a result achieving enhanced device performance and functionality. The nonlinear PD control method is experimentally implemented and the results demonstrate the effectiveness of the approach.
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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) | 0.001 | 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".