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Record W4416878298 · doi:10.37665/smihthd86872

Low Melting Temperature Interconnect Thermal Cycling Performance Enhancement Using Elemental Tuning and Edgebond Adhesive

2018· article· W4416878298 on OpenAlex

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 institutionsZymeworks (Canada)
Fundersnot available
KeywordsSolderingThixotropyInterconnectionAdhesiveBridging (networking)Melting temperatureRheologyTemperature cycling

Abstract

fetched live from OpenAlex

ABSTRACT Aggressive form factors, reducing pitch, thinner packages, and larger die to package ratios are leading to higher package warpage during SMT reflow. It is getting more challenging to mitigate warpage driven SMT defects viz. non-wet open (NWO), head-on-pillow (HoP) and solder bridging (SB). We studied multiple paste formulations using SMT hammer tests. Lab level characterizations were also used to establish a correlation between SMT performance and fundamental properties ofsolder pastes. We found that NWO and HoP compete with each other while having a correlation with flux activity to clean OSP on the Cu surface. SB risk showed a correlation with high temperature viscosity, indicating a rheology driven defect. Printability performance also showed a good correlation with the thixotropic index. These learnings will be extremely useful to develop next generation solder pastes to mitigate warpage driven defects.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
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 communication0.0000.001
Open science0.0010.000
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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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