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Record W2136961999 · doi:10.1109/coase.2008.4626443

Electrostatic torsional micromirror: Its active control and applications in optical network

2008· article· en· W2136961999 on OpenAlexaff
Ya‐Jun Pan, Yuan Ma, Shariful Islam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMicroelectromechanical systemsOptical switchControllabilityComb driveNonlinear systemMaterials scienceComputer scienceElectronic engineeringControl theory (sociology)OpticsEngineeringOptoelectronicsPhysicsControl (management)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.208
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2008
Admission routes1
Has abstractyes

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