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Record W1985992655 · doi:10.1115/detc2014-34283

A Low Voltage Electrostatic Micro Actuator for Large Out-of-Plane Displacement

2014· article· en· W1985992655 on OpenAlexafffund
Shahrzad Towfighian, Siyuan He, Ridha Ben Mrad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersMitacs
KeywordsActuatorVoltageDisplacement (psychology)Rotation (mathematics)PlanarStiffnessRotary actuatorLens (geology)OpticsAcousticsElectrical engineeringEngineeringPhysicsComputer scienceStructural engineering

Abstract

fetched live from OpenAlex

An electrostatic actuator is designed to move a 1 mm mirror, 58 μm out of plane at 25 volts. Large out-of-plane displacement is obtained from repulsive forces generated on four sets of comb drive fingers attached to the mirror plate in the middle. The proposed actuator is a customized design of a previous study for low voltage applications. The static modeling of the actuator was performed using a coupled-field finite element model of the actuator, including mechanical and electrical domains. Low voltage operation is achieved by decreasing the finger width and the lateral spacing, which increased the generated repulsive force at a specified voltage in a unit cell of the actuator. Decreasing the lateral spacing also enabled increasing the number of fingers, which could increase the repulsive-force, and consequently the torque and the rotation angles when the vertical gap between moving and fixed fingers is small. However, the redesigned actuator has a lower stiffness compared to the previous design. The actuator is optimized for auto-focusing applications in cell phone cameras that require voltages below 30 Volts for user safety. In the intended auto-focusing module, the actuators do not carry the lens and auto-focusing is obtained by moving the mirror attached to the actuators.

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: none
Teacher disagreement score0.697
Threshold uncertainty score0.354

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 head0.230
Teacher spread0.225 · 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

Citations12
Published2014
Admission routes2
Has abstractyes

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