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Record W4416879825 · doi:10.37665/jsmtywpng58628

iNEMI Pb-Free Alloy Alternatives Project Report: State of the Industry

2008· article· W4416879825 on OpenAlexaff
G.A. Henshall, Robert M. Healy, Ranjit Pandher, Keith Sweatman, Keith Howell, Richard Coyle, Thilo Sack, Polina Snugovsky, Stephen Tisdale, Fay Hua

Bibliographic record

VenueJournal of Surface Mount Technology · 2008
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsHain Celestial (Canada)
Fundersnot available
KeywordsSolderingReliability (semiconductor)AlloySupply chainBall grid arrayPrinted circuit board

Abstract

fetched live from OpenAlex

ABSTRACT Recently, the industry has seen an increase in the number of Pb-free solder alloy choices beyond the common near-eutectic Sn-Ag-Cu (SAC) alloys. New wave solder alloys have been developed with the intent of addressing concerns with copper dissolution, barrel fill, wave solder defects, and the high cost of alloys containing significant amounts of silver. Concerns regarding the poor drop/shock performance of near-eutectic SAC alloys led to the development of low Ag alloys to improve the mechanical strength of BGA and CSP solder joints. Most recently, investigations into new solder paste alloys for mass reflow have begun. The full impact of these materials on printed circuit assembly (PCA) reliability has yet to be determined. The increasing number of Pb-free alloys provides opportunities to address the important issues described above. At the same time, the increase in choice of alloys presents challenges in managing the supply chain and introduces a variety of risks, particularly to the reliability of PCAs. This paper provides the results of an iNEMI study of the present state of industry knowledge on Sn-Ag-Cu alloy “alternatives,” including an assessment of existing knowledge and critical gaps. Focus areas are recommended for closing these gaps, with the additional goal of avoiding repeated investigations into issues already resolved. Finally, efforts to update industry standards to account for the new alloys and to better manage supply chain complexity and risk are described.

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)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0170.008

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 head0.019
GPT teacher head0.250
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2008
Admission routes1
Has abstractyes

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