MétaCan
Menu
Back to cohort
Record W2161635030 · doi:10.1109/icmens.2003.1222032

Finite element modeling of a capacitive micromachined ultrasonic transducer

2004· article· en· W2161635030 on OpenAlexaff
B.J. Kirchmayer, Walied A. Moussa, M. David Checkel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCapacitive micromachined ultrasonic transducersCapacitive sensingRADIUSMaterials scienceFinite element methodUltrasonic sensorTransducerSurface micromachiningVoltageAcousticsElectrodeOptoelectronicsElectrical engineeringEngineeringPhysicsStructural engineeringFabrication

Abstract

fetched live from OpenAlex

Capacitive micromachined ultrasonic transducers have been investigated using finite element analysis. Three ANSYS models have been developed to illustrate the collapse voltage and operating frequency of CMUTs. A 2D axisymmetric model is used to illustrate the relationship between collapse voltage and electrode radius. The model agrees with past results for electrode radius sizes between full membrane radius and half membrane radius. A 3D solid model is utilized to demonstrate the resonant frequency of the CMUT silicon nitride membrane. A reduced order model is also used to depict the resonant frequency. Both models demonstrate a resonant operating frequency around 2.3 MHz for a membrane with a radius of 50 microns, a thickness of 1 micron, a residual stress of 60 MPa (tension) and biased at 30 V/sub DC/. The results are validated using previously reported findings.

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)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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

Citations8
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicUltrasonics and Acoustic Wave PropagationFrench-language works237,207