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Record W4416884581 · doi:10.37665/srnacpd58440

Fracture Performance of BGA/PCB Underfilled Assemblies

2014· article· W4416884581 on OpenAlexaff
Saeed Akbari, J.K. Spelt

Bibliographic record

VenueSoldering and Reliability Conferences · 2014
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBall grid arrayFlip chipSolderingFillet (mechanics)Fracture toughnessFracture mechanicsEpoxyFracture (geology)AdhesiveChip-scale package

Abstract

fetched live from OpenAlex

ABSTRACT The reliability of solder joints between a ball grid array (BGA) package and a printed circuit board (PCB) is commonly improved by bonding the BGA to the PCB using an underfill epoxy. Experimental fracture measurements were made using specimens prepared from a commercial multi-layer PCB assembled with thin profile fine-pitch ball grid array (TFBGA) packages. Two types of thermally cured capillary underfills were tested: underfill A was an unfilled epoxy, while underfill B contained 38 wt% silica particles. Fracture experiments were performed in a bending configuration under quasistatic loading conditions. In all specimens, the crack initiated within the underfill fillet, indicating the importance of cohesive fracture properties in controlling damage initiation. Finite element modeling showed that this crack initiation occurred under mode I conditions, with subsequent crack growth being mixed-mode in the PCB or at the interface between the PCB and the solder mask. The load corresponding to crack initiation in the underfill fillet for silica-filled underfill was significantly higher than unfilled underfill, indicating the toughening effect of the silica particles on the epoxy.

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 score0.002
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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