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Record W2085610212 · doi:10.1109/iecon.2012.6389253

Performance of an electrostatic actuated micromirror in a vacuum and non-vacuum packaging

2012· article· en· W2085610212 on OpenAlexafffund
Imran Khan, Jasmine Chong, R. Ben Mrad, Siyuan He, Michael J. Schertzer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
FundersCMC Microsystems
KeywordsOvershoot (microwave communication)Settling timeMaterials scienceOpticsVoltageOptoelectronicsPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Experimental results of micromirrors sealed in vacuum and non-vacuum environments are presented. The micromirror is 1.0 mm in diameter and is supported by four electrostatic repulsive actuators. The micromirror exhibits linear out-of-plane motion and rotational motion in two axes. The measured data shows that translational motion is 71 μm and 76 μm at 200 V for vacuumed and non-vacuumed micromirrors, respectively. The maximum optical rotational displacements for the vacuum and non-vacuum packaged micromirrors are 1.0° and 1.1°, respectively at 160 V. Packaging the micromirror in a vacuum decreases the squeeze film damping in the system. The settling time for the vacuum packaged micromirrors is 75 ms with an average overshoot of 116%. The settling times for the non-vacuumed micromirror are 2.75 ms with 5% overshoot for downward motion and 3.32 ms with 48% overshoot for upward motion. The estimated resonant frequency of the vacuum packaged micromirror is 2900 Hz, whereas the resonant frequency for the non-vacuumed micromirror is 1400 Hz. The static and dynamic results for the micromirror in reduced pressures determined that effects approaching the breakdown voltage become evident, resulting in lower displacements, and squeeze film damping effects are mitigated, leading to more consistent performance characteristics such as settling time and percent overshoot.

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 score0.068
Threshold uncertainty score0.343

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 head0.005
GPT teacher head0.214
Teacher spread0.208 · 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

Citations5
Published2012
Admission routes2
Has abstractyes

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