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Record W2899361845 · doi:10.1109/jmems.2018.2876384

A Novel Topology for Process Variation-Tolerant Piezoelectric Micromachined Ultrasonic Transducers

2018· article· en· W2899361845 on OpenAlexafffund
Alexandre Robichaud, Dominic Deslandes, Paul-Vahé Cicek, Frédéric Nabki

Bibliographic record

VenueJournal of Microelectromechanical Systems · 2018
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversité du Québec à MontréalÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à MontréalFonds de recherche du Québec – Nature et technologiesCMC Microsystems
KeywordsPMUTUltrasonic sensorPiezoelectricityTransducerCapacitive micromachined ultrasonic transducersMaterials scienceFabricationAcousticsSurface micromachiningNetwork topologyProcess (computing)Electronic engineeringTopology (electrical circuits)Computer scienceElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a novel architecture for piezoelectric micromachined ultrasonic transducers (PMUT) allowing for a drastic reduction in the impact of process variations on the accuracy of the resonant frequency. At the core of this new topology is a toroidal anchoring technique. Measurement results show that inter- and intra-die resonant frequency standard variations can be reduced from 101 kHz to 23 kHz and from 20 kHz to 5.9 kHz, respectively, by using the method proposed in this paper, showcasing devices fabricated using the PiezoMUMPs commercial fabrication technology. From the 16 chips that have been fabricated, each holding 12 PMUT devices of two different topologies, resonant frequency results are presented and analyzed to assess the effectiveness of the proposed approach. In addition, as a proof of concept, the fabricated PMUT are demonstrated to perform distance ranging measurements.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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
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

Citations14
Published2018
Admission routes2
Has abstractyes

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