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Record W2103957621 · doi:10.1109/esime.2007.359932

Evaluation of Thermal Strains in BGA Packages Using Digital Speckle Correlation Technique and FEA

2007· article· en· W2103957621 on OpenAlexaff
Adam R. Zbrzezny, Vincent Chan, Hua Lu, Ming Zhou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsToronto Metropolitan UniversityAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsBall grid arrayMaterials scienceFlip chipFinite element methodDigital image correlationTemperature cyclingComposite materialSpeckle patternChipThermalSolderingElectronic engineeringStructural engineeringOpticsElectrical engineeringThermodynamicsLayer (electronics)Engineering

Abstract

fetched live from OpenAlex

A digital speckle correlation (DSC) technique was applied to second level interconnects of flip chip and wire bonded packages in order to measure in-situ deformations induced by temperature cycling. The measurements allowed for the total normal and shear strains to be evaluated at various temperatures for different package types having lead-free and Sn-Pb metallurgies. It was observed that among the flip chip packages the shear strains varied with temperature and were greater for the Sn-Pb package than for the lead-free package. As expected, the BGA package with a metal ring stiffener exhibited the lowest strains. The results from the FEA model of the wire bonded PBGA correlated well with the experimental data obtained by DSC.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.270
Teacher spread0.243 · 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 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

Citations3
Published2007
Admission routes1
Has abstractyes

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