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Record W4413677328 · doi:10.1109/lsens.2025.3603120

A Method for Expanding the Bandwidth and Decreasing the Actuation Voltage of CMUT Devices

2025· article· en· W4413677328 on OpenAlexaff
Chirag Goel, Mathieu Gratuze, Alexandre Robichaud, Ricardo Izquierdo, Paul-Vahé Cicek

Bibliographic record

VenueIEEE Sensors Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsCapacitive micromachined ultrasonic transducersBandwidth (computing)VoltageElectrical engineeringElectronic engineeringComputer scienceOptoelectronicsMaterials scienceEngineeringTelecommunicationsCapacitive sensing

Abstract

fetched live from OpenAlex

[Figures/graphical abstract.pdf] This paper presents a novel capacitive micromachined ultrasonic transducer (CMUT) fabricated with the MEMSCAP PolyMUMPs process and incorporating two new design elements: spring arms and rocker stems. The structure enables low-Q-factor, broadband operation in a commercial surface-micromachining technology, making it suitable for air-coupled applications. The device operates at a bias voltage as low as 26 V, which is advantageous for portable systems. Electrical characterization demonstrates a$6.5\times$reduction in bias voltage compared with traditional designs. Four CMUTs were fabricated to isolate and assess the influence of each design element. The frequency spectra showed that the device with the novel design features had the broadest bandwidth (a$Q$-factor of approximately 1.2 compared to approximately 34 for the traditional design). These results highlight the potential of the proposed CMUT architecture for high axial resolution pulse-echo systems.

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 score0.004
Threshold uncertainty score0.012

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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