Low Melting Temperature Interconnect Thermal Cycling Performance Enhancement Using Elemental Tuning and Edgebond Adhesive
Bibliographic record
Abstract
ABSTRACT Aggressive form factors, reducing pitch, thinner packages, and larger die to package ratios are leading to higher package warpage during SMT reflow. It is getting more challenging to mitigate warpage driven SMT defects viz. non-wet open (NWO), head-on-pillow (HoP) and solder bridging (SB). We studied multiple paste formulations using SMT hammer tests. Lab level characterizations were also used to establish a correlation between SMT performance and fundamental properties ofsolder pastes. We found that NWO and HoP compete with each other while having a correlation with flux activity to clean OSP on the Cu surface. SB risk showed a correlation with high temperature viscosity, indicating a rheology driven defect. Printability performance also showed a good correlation with the thixotropic index. These learnings will be extremely useful to develop next generation solder pastes to mitigate warpage driven defects.
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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".