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Record W4243687891 · doi:10.1109/ultsym.2017.8092653

A nonlinear large signal equivalent circuit model for a square CMUT cell

2017· article· en· W4243687891 on OpenAlexaff
Mohammad Maadi, Roger J. Zemp

Bibliographic record

Venue2017 IEEE International Ultrasonics Symposium (IUS) · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCapacitive micromachined ultrasonic transducersEquivalent circuitFinite element methodAcousticsSquare (algebra)Nonlinear systemMaterials scienceCapacitive sensingSquare waveVoltageElectronic engineeringEngineeringElectrical engineeringPhysicsStructural engineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

Using analytical calculations, a precise large signal equivalent circuit model of square CMUT dynamics was developed. The model predicts many intrinsic properties of a square CMUT cell including resonance frequency, phase and magnitude of the membrane displacement, membrane velocity, electrical conductance, collapse voltage, etc. ANSYS 3D finite element analysis (FEA) was used to validate the equivalent circuit model predictions by performing static, pre-stressed harmonic and nonlinear transient analysis. The model was designed and implemented in a circuit simulator and then compared with finite element methods (FEM) and experimental results. The results were compared with circular CMUT cells when the half-side-length of the square CMUT is assumed to be equal to the radii of the circular CMUT cell. A silicon-nitride standard sacrificial release process was used to fabricate the square CMUT cells. The experimental results obtained by a laser vibrometer were compared with circuit simulations and the results showed excellent agreements.

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 score0.003
Threshold uncertainty score0.010

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.043
GPT teacher head0.286
Teacher spread0.244 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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