Polyimide Restructuring, An Application for Low-K/Ultra-Low-K Die and Lead-Free Bump Integrity Improvement
Bibliographic record
Abstract
ABSTRACT Polyimide is a widely used passivation layer on top of a semiconductor die. In a flip-chip package, this is used as a barrier to protect the die from stress brought about by the difference in the coefficient of thermal expansion between the silicon, the bump, the underfill, and the substrate. Use of polyimide has become even more popular with the use of the more brittle low-K dielectric on nanoscale die technology. A trade-off, however, is that the polyimide shows weakness in adhesion to the underfill compared to the conventional SiN/USG passivation layer. Now, while the industry is moving to the use of ultra-low-K dielectric as the die technology is shrinking to 45 nm and below, and while the use of lead-free bump earns more and more attention, the industry is searching for a solution to protect simultaneously the low-K/ultra-low-K die and the lead-free bump. The brittleness and weakness of the IMC layer of a lead-free bump has been one of the biggest concerns in the transition to lead-free bump. Lead-free solder materials are also relatively less ductile than eutectic solder; this increased stiffness can lead to significant stresses that make the brittle intermetallic crack, particularly when operating temperatures are high or there is a significant mismatch between the coefficients of thermal expansion between the die and the substrate. This paper will discuss how a restructured polyimide layer can offer a stress buffer for both the die and the lead-free bump, including discussions on how the polyimide can be restructured to improve its adhesion to the underfill.
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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) | 0.003 | 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".