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Record W2057680014 · doi:10.1115/imece2008-68081

A Linearly Tunable MEMS Capacitor With Segmented Electrode and Enhanced Structure

2008· article· en· W2057680014 on OpenAlexaff
Mohammad Shavezipur, Seyed Mohammad Hashemi, Amir Khajepour, Patricia Nieva

Bibliographic record

VenueVolume 13: Nano-Manufacturing Technology; and Micro and Nano Systems, Parts A and B · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
Fundersnot available
KeywordsCapacitorCapacitanceCantileverMicroelectromechanical systemsStiffnessMaterials scienceLinearityVoltageElectrodeNode (physics)Electronic engineeringOptoelectronicsStructural engineeringElectrical engineeringEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

This research presents a novel linearly tunable MEMS capacitor with flexible electrode and modified structural stiffness. The capacitor is designed for PolyMUMPs as a standard three-structural-layer fabrication process. The moving electrode is divided into segments interconnected through torsional springs. Under each connecting point (node) two flexible and rigid steps are located. The flexible steps are cantilever beams, and as the bias voltage increases, they touch their corresponding nodes and consequently their stiffnesses are added to the total structural stiffness. This is the core idea of the proposed design to linearize the capacitance-voltage (C-V) response. An analytical model is developed to investigate the behavior of the new capacitor. In this model, the governing equations of the capacitor are numerically solved to obtain the system’s C-V response. An optimization problem with different design variables, such as dimensions of the segments or the beams stiffness coefficients, is solved to maximize the linearity of the C-V curves. The numerical results demonstrate drastic improvement in capacitors performance, where a highly linear C-V response and a maximum tunability of 94% is reached.

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 categoriesMeta-epidemiology (narrow)
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 score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.170
Teacher spread0.165 · 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.

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

Citations0
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueVolume 13: Nano-Manufacturing Technology; and Micro and Nano Systems, Parts A and BSame topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207