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Record W4416878207 · doi:10.37665/smtopvg23324

Developing High Reliability Solders for Harsh Environment

2018· article· W4416878207 on OpenAlex
Mehran Maalekian

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0010.000
Research integrity0.0000.000
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 head0.021
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