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Record W4416876056 · doi:10.37665/smbwmyd48156

High-Reliability, Fourth Generation Low-Temperature Solder Alloys

2020· article· W4416876056 on OpenAlexaff
Morgana Ribas, Prathap Augustine, Pritha Choudhury, Siuli Sarkar

Bibliographic record

VenueSMTA International · 2020
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsAlpha Technologies (Canada)
Fundersnot available
KeywordsSolderingEutectic systemTemperature cyclingBrittlenessAlloyThird generationThermal shock

Abstract

fetched live from OpenAlex

ABSTRACT The eutectic 42Sn58Bi alloy showed promising results as a low temperature solder in the first generation of lead-free solders, but its excessive brittleness has greatly limited its use in the electronics industry. Minor changes in the eutectic SnBi, such as addition of Ag or other micro-additives, was shown to successfully improve its thermal and mechanical reliability, although still far from the SAC305 performance. Driven by increased miniaturization, complexity, and design integration in electronics, a third generation of SnBi alloys has been recently introduced and shown to enable low temperature soldering while delivering superior drop shock and thermal cycling performance. In this paper, a fourth generation of SnBi solder alloys is introduced, and its performance is compared with previous SnBi solders and SAC305. Testing including melting behaviour and solder joint formation, tensile tests at room temperature and at 75oC, high temperature creep, and other physical properties are discussed here.

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 score0.001
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.224
Teacher spread0.209 · 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
Published2020
Admission routes1
Has abstractyes

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