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Record W7037022518

Design, modeling and fabrication of polymer-based Capacitive Micromachined Ultrasonic Transducers (polyCMUTs)

2018· other· en· W7037022518 on OpenAlexaff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2018
Typeother
Languageen
FieldComputer Science
TopicBayesian Modeling and Causal Inference
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFabricationCapacitive micromachined ultrasonic transducersUltrasonic sensorCapacitive sensingTransducerUltrasoundWearable computerElectronicsSurface micromachining
DOInot available

Abstract

fetched live from OpenAlex

Ultrasound imaging is the most widely used medical imaging modality in the world. Modern ultrasound systems still rely on the same piezoelectric-based technology since their creation in the 1930s. Despite their mature technology, they are expensive to fabricate, difficult to create 2D arrays and cannot be miniaturized. Capacitive Micromachined Ultrasonic Transducers (CMUTs) are considered the replacement of piezoelectric transducers given their high bandwidth, ease of integration with electronics and miniaturization. The main focus of this dissertation involves the simulation, fabrication and characterization of polymer-based CMUTs (polyCMUTs). A new fabrication process involving inexpensive polymer materials and minimum fabrication steps was developed. The fabrication procedure allows the creation of biocompatible ultrasound chips in a few hours and with costs well below $100 USD, having a performance comparable to current commercial devices. The fabricated polyCMUTs exhibit a phenomenon termed “pre-biasing”, which allowed the operation of polyCMUTs as passive devices (no external power needed). The first B-mode ultrasound image in the world created using polyCMUTs is also presented. As a future plan, the development of a low-cost wearable ultrasound health monitoring system is conceived.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.014
GPT teacher head0.187
Teacher spread0.172 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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