Electrostatic Torsional Micromirror With Enhanced Tilting Angle Using Active Control Methods
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it