MétaCan
Menu
Back to cohort
Record W4416878207 · doi:10.37665/smtopvg23324

Developing High Reliability Solders for Harsh Environment

2018· article· W4416878207 on OpenAlexaff
Mehran Maalekian

Bibliographic record

VenueSMTA International · 2018
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsComputer Research Institute of Montréal
Fundersnot available
KeywordsSolderingAlloyReliability (semiconductor)MicrostructureAutomotive industryWork (physics)

Abstract

fetched live from OpenAlex

ABSTRACT Applications such as high power LED lighting and under hood automotive require a solder alloy to operate at temperatures higher than 150 °C where common leaded and lead-free alloys would be prone to failure. In order to develop a suitable solder alloy for harsh environment we need to understand the role of each alloying element added into the solder alloy and identify to what extend can benefit a solder joint from metallurgical and reliability point of view. Therefore, in this work a systematic study on the effects of Bi, Sb, Ag, Cu and Ni on mechanical and thermal behavior of Sn-based alloys is presented. Based on this systematic approach and other research presented earlier a new multicomponent solder alloy for demanding applications is presented. In this metallurgical approach, the evolution of microstructure and mechanical properties with alloying elements and thermal effects are emphasized as the key parameters when developing solder alloys for demanding applications.

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 score0.003

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 communication0.0010.000
Open science0.0000.000
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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueSMTA InternationalSame topicElectronic Packaging and Soldering TechnologiesFrench-language works237,207