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Record W2742069874 · doi:10.1139/tcsme-2014-0002

DESIGN, SIMULATION AND BIFURCATION ANALYSIS OF A NOVEL MICROMACHINED TUNABLE CAPACITOR WITH EXTENDED TUNABILITY

2014· article· en· W2742069874 on OpenAlexfundvenueno aff
Hamed Mobki, Kaveh Rashvand, Saeid Afrang, Morteza Sadeghi, Ghader Rezazadeh

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsnot available
FundersUniversité de Montréal
KeywordsCantileverCapacitanceMaterials scienceCapacitorCapacitive sensingVariable capacitorBeam (structure)BifurcationStiffnessMicroelectromechanical systemsVoltageElectrodeOptoelectronicsComposite materialOpticsPhysicsElectrical engineeringNonlinear systemEngineering

Abstract

fetched live from OpenAlex

In this paper, a novel RF MEMS variable capacitor has been presented. The applied techniques for increasing the tunability of the capacitor are the increasing of the maximum capacitance and decreasing of the minimum capacitance. The proposed structure is a simple cantilever Euler–Bernoulli micro-beam suspended between two conductive plates, in which the lower plate is considered as stationary reference electrode. In this structure, two pedestals are located in both tips of the cantilever beam. In the capacitive micro-structures, increasing the applied voltage decreases the equivalent stiffness of the structure and leads the system to an unstable condition (pull-in phenomenon). By deflecting the beam toward the upper (lower) plate the minimum (maximum) capacitance decreases (increases) and tunability increases consequently. The located pedestals increase and decrease the maximum and minimum capacitance respectively. The results show that the proposed structure increases the tunability of cantilever beam significantly. Furthermore, bifurcation behavior of movable electrode has been investigated.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score0.341

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 head0.013
GPT teacher head0.212
Teacher spread0.200 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations10
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207