Electrostatic torsional micromirror: Its active control and applications in optical network
Bibliographic record
Abstract
Electrostatically actuated torsional micromirror fabricated using microelectromechanical systems (MEMS) technology is a fundamental building block for many optical network applications, such as optical wavelength-selective switch, configurable optical add-drop multiplexers and optical cross-connects. The major technical obstacle to achieve its full potentials in both functionalities and performance is the controllability and stability of its tilting angle. This paper presents the model for a micromirror fabricated using micragem silicon-on-insulator process. Closed-loop control approaches are proposed for the 1-degree of freedom (DOF) MEMS device assuming that the angle position can be measured. Compared with traditional open loop control approaches, the nonlinear proportional and derivative (PD) control and the gain scheduling approach improve the performance of the mirror switching, and enhance the robustness of the structures to any stochastic perturbations. Furthermore, the nonlinear PD control can achieve a larger controllable tilting angle than the pull-in angle resulting in significantly enhanced device performance and functionality. Applications of the electrostatic torsional micromirror to optical network are further discussed to underscore the significance and necessities of such methods.
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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 | 0.000 | 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".