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Record W2026820422 · doi:10.1115/1.4000281

Finite Element Modeling of Simultaneous Ultrasonic Bumping With Au Balls

2009· article· en· W2026820422 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Electronic Packaging · 2009
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsBumpingMicroelectronicsFinite element methodMaterials scienceInterconnectionDisplacement (psychology)Stress (linguistics)ModulusUltrasonic sensorStructural engineeringMechanical engineeringComposite materialEngineeringAcousticsOptoelectronics

Abstract

fetched live from OpenAlex

Bumping of microcircuits and substrates establishes interconnect points required for subsequent bonding of microelectronic components, allowing for power and data distribution. Simultaneous ultrasonic bonding of individual Au balls promises to accelerate bumping processes and is studied using a finite element model. The model covers the static forces at the end of a successful bonding operation and analyzes the interfacial stresses between bumps and substrate. The modeling shows the vertical forces acting on the bumps when a lateral displacement of the bonding tool is applied. When designing a practical bonding application, the control of such vertical forces is recommended. A sensitivity analysis is conducted to study the effect of the main factors on the model responses. This analysis reveals that variations in bump height and bonding tool elastic modulus are the major factors affecting the forces on the bumps.

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.

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)0.001
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 score0.268
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.215
Teacher spread0.207 · 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