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Record W2011541785 · doi:10.1115/detc2002/mech-34293

Mechanical Design and Modeling of a New MEMS Vertical Actuator

2002· article· en· W2011541785 on OpenAlexaff
Yan Dong, Amir Khajepour, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsActuatorDeflection (physics)Microelectromechanical systemsCapacitorThermalMechanical engineeringThermal resistanceMaterials scienceEngineeringHeat transferElectrical engineeringVoltageMechanicsOptoelectronicsPhysicsOptics

Abstract

fetched live from OpenAlex

This paper presents a new micro electro-thermal actuator with vertical motion. In the traditional vertical electro-thermal actuators, the arms of actuators are fabricated with different widths to provide high and low electrical resistance for the hot and cold arms. Applying electrical current to the arms results in different thermal expansion between the hot and cold arm, which forces the tip of the device to bend. The new vertical electro-thermal actuator design eliminates the parasitic electrical resistance of the cold arm through the use of a ‘U’ shape structure. This U-shape structure results in a bi-directional vertical actuator with larger deflection and improved electrical efficiency by providing an active return current pass. The ‘U’ shape actuator is more efficient in applications such as variable capacitors where larger deflection can increase the tuning range. For analysis and design purposes, an effective method to transfer the continuous system to a lumped model is presented. Simulation results are provided to show the deflection and effects of design parameters on the actuator performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.238
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2002
Admission routes1
Has abstractyes

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