Low Melting Temperature Solder Interconnect Behavior and Thermal Cycling Performance Enhancement Using Edgebond
Bibliographic record
Abstract
ABSTRACT Low melting temperature solder, which enable a lower temperature assembly process comes with significant benefits to less warpage and lower component defect risk, but counter with an potential inferior thermal cycling performance due to the higher creep rate at elevated temperature environment. Adding to the recent active efforts to improve the thermal cycling performance with maintaining the melting temperature, the degradation mechanism in low melting temperature solder alloys is critical to understand, because the mitigation of the degradation mechanism will provide the key mechanism improving the thermal cycling performance. In this study, a series of ball shear test were performed on Sn-58Bi solder balls with 300μm diameter along with SAC3905 solder balls. To see the solder mechanical stability at elevated temperature, a heating stage was used to provide isothermal temperature during shear testing at 100oC. The maximum shear load and the distance to the peak shear load were measured to see the behavior change at elevated temperature. The shear results show an overall higher shear load value with Sn-58Bi compared to SAC305, and a decrease in ductility with elevated temperature shear. Further isothermal aging at 100oC for 200hours decreased the maximum shear load further and increased the brittle behavior in Sn-58Bi. With the understanding of the Sn-58Bi behavior, 12x12 mm chip array BGA (CABGA) components on 62mil thick boards were thermal cycled from -40ºC to 100ºC with Sn-58Bi based alloys to evaluate the thermal cycling performance. To enhance the thermal cycling performance, an edgebond adhesive was applied. Compared to the characteristic life cycle number of 3327 cycles with Sn-58Bi solder, the full edgebond components do not show any failures up to the test completion at 4050 cycles.
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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 | 0.000 |
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".