Flip Chip Bump Formation of Sn–1.8Bi–0.8Cu–0.6In Solder by Stencil Printing
Bibliographic record
Abstract
Flip chip bumping by stencil printing method using a new composition of solder paste, Sn–1.8%Bi–0.8%Cu–0.6%In, all in mass%, was investigated. Sn–3.5%Ag, Sn–37%Pb and Sn–36%Pb–2%Ag were selected as references for the experiment. The solder pastes were printed on the under bump metallization (UBM) of a Si-wafer using a stencil, where diameter and thickness of the stencil opening were 400 and 150 μm, respectively. The UBM deposit comprised 0.4 μm each of Al, Ni and Cu, and 20 nm of Au from bottom to top of the metallization, sequentially. The printed paste bumps were reflow soldered in air, and the peak soldering temperature of Sn–1.7Bi–0.8Cu–0.6In and Sn–3.5Ag was 523 K and of Sn–37Pb and Sn–36Pb–2Ag was 503 K. From the experimental results the solder bumps of Sn–1.8Bi–0.8Cu–0.6In alloys were well-formed with a mean height of 260 μm. The shear strength of Sn–1.8Bi–0.8Cu–0.6In at 523 K (as-reflowed) showed the highest value of 6.5N followed by those of Sn–3.5Ag, Sn–37Pb and Sn–36Pb–2Ag solders. After 1000 h aging, while the shear strength of the Sn–1.8Bi–0.8Cu–0.6In showed 27% decrease compared to as-reflowed conditions, it was still 15–30% higher than those of Sn–37Pb, Sn–36Pb–2Ag and Sn–3.5Ag solders. Intermetallic compounds (IMCs) formed on the interface between solder and UBM were (Cu,Ni)6Sn5. As aging time went on up to 1000 h, the content of Ni in the IMC changed from 6.6% at initial stage (as-reflowed) to 13.5% at final stage (1000 h aging).
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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.
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".