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Record W4310585037 · doi:10.1109/ius54386.2022.9957587

Metallurgical AuSn Bonding of Piezoelectric Layers

2022· article· en· W4310585037 on OpenAlexfundno aff
Per Kristian Bolstad, Martijn Frijlink, Lars Hoff

Bibliographic record

Venue2022 IEEE International Ultrasonics Symposium (IUS) · 2022
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsnot available
FundersCanadian Institute of Ukranian Studies, University of AlbertaNorges Forskningsråd
KeywordsMaterials scienceScanning acoustic microscopeAcoustic microscopySolderingComposite materialEutectic systemPiezoelectricityAcoustic impedanceOptical microscopeStack (abstract data type)Electrical impedanceTransducerScanning electron microscopeDelamination (geology)Electrical conductorMicroscopyAcousticsMicrostructureOpticsElectrical engineering

Abstract

fetched live from OpenAlex

This study presents a novel and simple approach to form metallurgical bonds between layers of the acoustic stack in an ultrasound transducer using a eutectic AuSn preform, which is a pre-shaped thin sheet of solder. The bonding takes 10 minutes and yields uniform bondlines of 10 μm with a melting temperature of 280 °C. The resulting bonds are electrically conductive, mechanically robust and have a characteristic acoustic impedance of 29 Mrayls. AuSn bonding was demonstrated by successfully bonding two equally thick layers of PZT. The bonded PZT-stacks were polarized and sub-diced into a 2-dimensional array. Characterization was performed by optical microscopy, scanning acoustic microscopy and electrical impedance measurements. Combining scanning acoustic microscopy and electrical impedance measurements proved to be a useful approach to experimentally study inter-element variations induced by varying concentrations of voids in a metallurgical bondline.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.227
Teacher spread0.217 · 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
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

Citations0
Published2022
Admission routes1
Has abstractyes

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