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Record W1969570941 · doi:10.1108/09540910510597456

Fluxless plasma bumping of lead‐free solders and the reliability effects of under bump metallization thickness

2005· article· en· W1969570941 on OpenAlexaff
Joon Kwon Moon, Y. Zhou, Jae Pil Jung

Bibliographic record

VenueSoldering and Surface Mount Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBumpingMaterials scienceSolderingShear strength (soil)MetallurgyComposite materialJoint (building)Reflow solderingStructural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Purpose To investigate fluxless plasma ball bumping and effect of under bump metallization (UBM) thickness on joint properties using lead‐free solders. Design/methodology/approach A fluxless soldering process was investigated in this study using Ar‐10 percent H 2 plasma reflow. Balls made from two lead‐free solders (Sn‐3.5 weight percent Ag and Sn‐3.5 weight percent Ag‐0.7 weight percent Cu) were reflowed and, also Sn‐37 weight percent Pb as a reference. In particular, the effects of the UBM thickness on the interfacial metallurgical bonding and joint strength were studied. The UBM (Au/Cu/Ni/Al layers) thicknesses were 20 nm/0.3 μ m/0.4 μ m/0.4 μ m and 20 nm/4 μ m/4 μ m/0.4 μ m, respectively. Findings The experimental results showed that in the case of a thin UBM the shear strengths of the soldered joints were relatively low (about 19‐27 MPa) due to cracks observed along the bond interfaces. The thick UBM improved joint strength to 32‐42 MPa as the consumption of the Cu and Ni layers by reaction with the solder was reduced and hence the interfacial cracks were avoided. To provide a benchmark, reflow of the solders in air using flux was also carried out. Originality/value This paper provides information about the effect of UBM thickness on joint strength for plasma fluxless soldering to researchers and engineers.

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 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 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 score0.232
Threshold uncertainty score0.771

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.189
Teacher spread0.185 · 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 teacher head, 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

Citations4
Published2005
Admission routes1
Has abstractyes

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