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Record W4416879449 · doi:10.37665/rezqtqh29421

Process Considerations in Reducing Voiding in High Reliability Lead Free Solder Joints

2015· article· W4416879449 on OpenAlexaff
William W. Yu, Steve D. M. Brown, Mitch Hohzer

Bibliographic record

VenueRegional Events · 2015
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsAlpha Technologies (Canada)
Fundersnot available
KeywordsSolderingSolder pasteReliability (semiconductor)Ball grid arraySurface-mount technologyProcess (computing)Reflow solderingLead (geology)

Abstract

fetched live from OpenAlex

ABSTRACT This paper is in English and Chinese languages. With the increasing transition of European and American automotive OEMs to ROHS compliant soldering materials, high reliability tin/silver/copper alloys have been widely examined. One six part alloy known as Innolot has become widely adapted because of its high resistance to thermal cycle fatigue and resistance to high temperature creep. Examining data from numerous field trials has shown that solder pastes using Innolot alloy tend to produce more voids in bottom terminated components (BTCs) when compared to the same paste flux vehicle used with SAC 305 alloy. Several techniques have been reported for reducing voids in BGA components, but many of these techniques do not seem to reduce voiding when Innolot solder paste is used in conjunction with BTCs. Video data showing the formation of voids in a simulated BTC reflow process led to the development of a different type of reflow profile strategy. Data mining of field reports indicated that this strategy had worked several times without the need to reflow the solder joint under vacuum. A controlled experiment using five different reflow profiles and a previously used BTC voiding test vehicle was undertaken to validate the field observations. This paper will show the field data and the results of the controlled experiments, giving the reader a new reflow strategy for reducing BTC voids when using Innolot based solder paste.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.049
GPT teacher head0.281
Teacher spread0.231 · 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.

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
Published2015
Admission routes1
Has abstractyes

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