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Record W2904085559 · doi:10.1109/antem.2018.8572954

Modeling of Frequency Shift in RF-MEMS Switches Under Residual Stress Gradient

2018· article· en· W2904085559 on OpenAlexaff
Tejinder Singh, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsResidual stressMicroelectromechanical systemsFinite element methodRadio frequencyMaterials scienceCurvatureStress (linguistics)ResidualMechanicsAcousticsElectronic engineeringStructural engineeringElectrical engineeringOptoelectronicsEngineeringComputer sciencePhysicsComposite materialMathematicsGeometry

Abstract

fetched live from OpenAlex

Residual stress gradient is unavoidable in thin-film based devices such as radio frequency (RF) micro electro mechanical systems (MEMS) switches. In this paper residual stress gradient is modelled which affect the frequency shift in RF-MEMS switches. Curvature of thinfilms are measured experimentally and then residual stress gradient is modelled analytically and using finite element modeller (FEM). A comparison between analytical model and FEM has been carried out by introducing the initial upward curvature due to residual stress in the model. Beams under residual stress gradient change the pull-in voltage and shows frequency shift which points out that underestimating such effects can lead to inaccurate design and analysis of RF-MEMS switches and subsystems.

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 score0.386
Threshold uncertainty score0.362

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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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
Published2018
Admission routes1
Has abstractyes

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