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Record W1998233388 · doi:10.1016/j.proeng.2012.09.204

Capacitive Micromachined Ultrasonic Transducer Array with Pencil Beam Shape and Wide Range Beam Steering

2012· article· en· W1998233388 on OpenAlexafffund
Tahereh Arezoo Emadi, Gabriel Thomas, Stephen Pistorius, D. A. Buchanan

Bibliographic record

VenueProcedia Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsCapacitive micromachined ultrasonic transducersTransducerBeam steeringCapacitive sensingMaterials scienceUltrasonic sensorAcousticsPencil (optics)Beam (structure)Surface micromachiningOpticsEngineeringElectrical engineeringPhysicsFabrication

Abstract

fetched live from OpenAlex

Abstract A capacitive micromachined ultrasonic transducer (CMUT) array is designed as an alternative to conventional piezoelectric transducers. A thin silicon nitride membrane is suspended over a bottom electrode on a silicon wafer. In the immersion mode, the transducer cell shape and dimensions are optimized for an operating frequency of 10 MHz. We show that the proposed imager array can generate a pencil shape beam with a ∼1.5° half beam width, enhancing the detector resolution. A phased array technique is employed to excite multiple cells using time-delayed signals to steer the acoustic beam toward the object. This eliminates the need to mechanically move the detector, simplifying the transducer driving system. Moreover, unlike conventional transducers, the pencil beam can be effectively steered over a wide range of angles without producing grating lobes, which minimizes power loss in undesired directions. This can also improve the signal to noise ratio of the imager CMUT array.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.165
Teacher spread0.160 · 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 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

Citations3
Published2012
Admission routes2
Has abstractyes

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