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Record W3047798845 · doi:10.1109/ectc32862.2020.00116

Heterogenous Bump Metallurgy Through a Sequential Plating Based Process

2020· preprint· en· W3047798845 on OpenAlexaff
Abderrahim El Amrani, Etienne Paradis, David Danovitch, Dominique Drouin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsElectromigrationMaterials scienceSolderingFlip chipInterconnectionPlating (geology)FabricationDiffusion barrierThermal copper pillar bumpDwell timeReflow solderingMetallurgyDiffusionOptoelectronicsComposite materialLayer (electronics)Computer science

Abstract

fetched live from OpenAlex

A novel, heterogeneous solder bump structure and means to achieve is proposed. By exploiting a sequential plating process for bump fabrication, a diffusion barrier is placed between solder structures comprising different Ag contents. By controlling the nature and shape of the barrier, a specific structure was derived that successfully maintained a low Ag, ductile solder region proximal to the fragile BEOL of the chip during all solder reflow processes that would occur prior to a reinforcing underfill. A subsequent long dwell reflow step was demonstrated to be capable of breaking the barrier to allow Ag diffusion from the high Ag content region, thus creating a homogeneous interconnect structure with sufficiently high Ag content to encourage high electromigration resistance. The derived temporary structure has the added advantage of ensuring a high aspect ratio, pillar-like solder structure that enables a high-density interconnect design without the use of a stiff Cu structure.

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 score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.001
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 head0.049
GPT teacher head0.275
Teacher spread0.226 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same topicElectronic Packaging and Soldering TechnologiesFrench-language works237,207