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Record W1861677815 · doi:10.1016/j.phpro.2015.08.194

Fabrication of new Interdigital Transducers for Surface Acoustic Wave Device

2015· article· en· W1861677815 on OpenAlexaff
Lamia El Fissi, Abdelatif Jaouad, Denis Vandormael, Laurent A. Francis

Bibliographic record

VenuePhysics Procedia · 2015
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceSurface micromachiningSurface acoustic wavePolishingElectroplatingTransducerEtching (microfabrication)FabricationChemical-mechanical planarizationOptoelectronicsReactive-ion etchingSubstrate (aquarium)Interdigital transducerIsotropic etchingLayer (electronics)AcousticsComposite materialPiezoelectricity

Abstract

fetched live from OpenAlex

We investigate high-performance interdigital transducers (IDTs) for the generation of surface acoustic waves (SAWs) on AT-cut quartz, where the metal fingers are embedded in the substrate. Three micromachining techniques are used to manufacture SAW structures, namely an inductively coupled plasma, a laser etching and a reactive ion etching. An evaporated layer of Al and a Ni thick electroplating are used to grow the metals in the micromachining structures. A chemical mechanical polishing (CMP) technique is used to remove the exceeding metal and keep a flat surface. The electrical characterizations indicate that the fabricated devices are suited for sensing proposes with a low insertion loss and a linear phase. Results are reported emphasizing the efficiency of the Ni damascene process to manufacture SAW sensors with the embedded structures.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.054
GPT teacher head0.258
Teacher spread0.204 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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