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Record W2011451128 · doi:10.1088/0960-1317/18/4/045012

The effects of non-uniform nanoscale deflections on capacitance in RF MEMS parallel-plate variable capacitors

2008· article· en· W2011451128 on OpenAlexafffund
Amro M. Elshurafa, E.I. El-Masry

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsCapacitanceCapacitorMicroelectromechanical systemsMaterials scienceCapacitive sensingVariable capacitorResidual stressFinite element methodStress (linguistics)VoltageStructural engineeringElectrical engineeringPhysicsOptoelectronicsComposite materialEngineeringElectrode

Abstract

fetched live from OpenAlex

This paper analyzes in detail the effect of nanoscale non-uniform deflections, caused by the residual stress phenomenon and/or applied actuation dc voltages, on the capacitance in RF MEMS parallel-plate variable capacitors in an effort to determine the severity of this effect on the ideal parallel-plate capacitance expression normally used.Closed-form capacitance expressions and integrals are given for second order, fourth order, elliptic paraboloidal and hyperbolic paraboloidal models of the bending behavior of the top plate caused by the residual stress and/or actuation.The theoretical analysis is then verified using the finite-element modeling method and results from both analyses exhibit excellent agreement.The equations obtained are then applied to a fabricated chip and other fabricated capacitors in the literature.It was found that anticipating the capacitance using the ideal parallel-plate formula can deviate from the capacitance incorporating the deflections by as much as 80%.The presented equations better anticipate the real capacitance of actual fabricated chips and can take into account deflections in the top plate even if they were in the order of a few nanometers.

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 score0.023
Threshold uncertainty score0.532

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 head0.005
GPT teacher head0.182
Teacher spread0.178 · 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

Citations11
Published2008
Admission routes2
Has abstractyes

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