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Record W2022243882 · doi:10.1109/jmems.2015.2392692

A 3-DOF MEMS Electrostatic Piston-Tube Actuator

2015· article· en· W2022243882 on OpenAlexaff
Faez Ba-Tis, Ridha Ben-Mrad

Bibliographic record

VenueJournal of Microelectromechanical Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsActuatorMicroelectromechanical systemsMaterials sciencePiston (optics)WaferStiffnessFabricationPneumatic actuatorMechanism (biology)Tube (container)Silicon on insulatorTranslation (biology)Rotation (mathematics)Comb driveMechanical engineeringOptoelectronicsOpticsComposite materialSiliconElectrical engineeringPhysicsEngineeringComputer science

Abstract

fetched live from OpenAlex

A three-degrees-of-freedom microelectromechanical systems (MEMS) electrostatic actuator was developed, fabricated, and tested. The actuator utilizes a piston-tube configuration that enables the use of electrodes with a wide area. Therefore, a large output force can be generated. The actuator also utilizes a flexure mechanism in which the in-plane (x-y) stiffness is high compared with the out-of-plane (z-axis) stiffness. This flexure mechanism enables a large translation stroke along the z-axis before experiencing lateral pull-in instability. A prototype of the actuator was able to achieve a pure translation (piston-style motion) of 28 μm at 80 V and an angle of rotation of 0.78° at 70 V. A mass of 1 mg was translated 24 μm at 80 V, indicating the ability of the actuator to provide high output force. The actuator was fabricated using the Micralyne MicraGEM-Si MEMS fabrication process, in which the pistons and tubes are fabricated in the device layers of two bonded Silicon on insulator (SOI) wafers. The misalignment between the pistons and the tubes using this process was ~0.005 of the gap between adjacent pistons and tubes. This amount of misalignment leads to a negligible lateral instability effect.

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)0.001
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 score0.094
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.224
Teacher spread0.209 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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