Fluxless plasma bumping of lead‐free solders and the reliability effects of under bump metallization thickness
Bibliographic record
Abstract
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 H2 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 μm/0.4 μm/0.4 μm and 20 nm/4 μm/4 μm/0.4 μm, 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".