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Record W2290765774 · doi:10.1109/robio.2015.7418897

A FEM simulation approach for multilayered SAW delay line devices

2015· article· en· W2290765774 on OpenAlexfundno aff
Bing Zhang, Hong Hu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsnot available
FundersCMC Microsystems
KeywordsSurface acoustic waveAcousticsReflection coefficientSurface acoustic wave sensorTransducerSIGNAL (programming language)Reflection (computer programming)Coupling (piping)Finite element methodCoupling coefficient of resonatorsMaterials scienceInterference (communication)Electronic engineeringComputer scienceEngineeringElectrical engineeringPhysicsOptoelectronicsResonator

Abstract

fetched live from OpenAlex

The active compliance control is popular in surgical robots, which requires novel wireless and passive pressure sensor. In this paper, a simulation of surface acoustic wave multilayered device is presented. It includes a SiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> / Al electrode/ YZ-LiTaO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> , which has a high coupling coefficient as well as a low temperature coefficient of delay. An eigenfrequency analysis is done first to calculate the phase velocity and input frequency of the RF signal. A time domain analysis is pursued next to generate the surface acoustic waves and the characteristics of wave propagation. In this process, the boundary reflection is eliminated by adding perfectly matched layers and appropriate damping. This approach is essential for assessment of SAW pressure sensor design configurations prior to prototyping.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.407

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 head0.066
GPT teacher head0.285
Teacher spread0.219 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2015
Admission routes1
Has abstractyes

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