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Fabrication of capacitive micromachined ultrasonic transducers based on adhesive wafer bonding technique

2016· article· en· W2538415792 on OpenAlex
Zhenhao Li, Lawrence L. P. Wong, Albert I. H. Chen, Shuai Na, Jame Sun, John T. W. Yeow

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsMaterials scienceCapacitive micromachined ultrasonic transducersWaferFabricationCapacitive sensingUltrasonic sensorWafer bondingTransducerBenzocyclobuteneOptoelectronicsAdhesiveComposite materialAcousticsDielectricElectrical engineeringPiezoelectricity

Abstract

fetched live from OpenAlex

This paper reports the fabrication process of wafer bonded capacitive micromachined ultrasonic transducers (CMUTs) using photosensitive benzocyclobutene as a polymer adhesive. Compared with direct bonding and anodic bonding, polymer adhesive bonding provides good tolerance to wafer surface defects and contamination. In addition, the low process temperature of 250 °C is compatible with standard CMOS processes. Single-element CMUTs consisting of cells with a diameter of 46 µm and a cavity depth of 323 nm were fabricated. In-air and immersion acoustic characterizations were performed on the fabricated CMUTs, demonstrating their capability for transmitting and receiving ultrasound signals. An in-air resonance frequency of 5.47 MHz was measured by a vibrometer under a bias voltage of 300 V.

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.

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 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: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.189
Teacher spread0.184 · 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