Development and Screening of Polymer Collar WLP1 Candidates for Lead-Free Solder Sphere Technology to Enhanced Reliability
Bibliographic record
Abstract
ABSTRACT Polymer reinforcement at the solder bump (sphere) joint is called a “polymer collar.” This paper describes the affects of visco-elastic properties of two types of thermoset resin blends (J-series and C-series) on die yield for a polymer collar wafer level package (WLP) 1 using a lead-free process. The criteria for determining die yield are discussed and illustrated. In general, good polymer collar material performance is defined where first; the solder (sphere) effectively wets the copper pad. Secondly, the polymer forms a fillet or collar at the solder ball/die interface, without encapsulating the solder ball. Based on the viscoelastic data on several polymer candidates (from two different epoxy/flux families) as compared to die yield percentage, it is clear that minor changes in the ratios of components in the polymer candidates we tested have a huge effect. We have also concluded that a very low complex modulus [G* = ((G′) 2 + (G″) 2 ) 1/2 ] slope change over time (ΔG*/Δt) and temperature, produced by the thermoset reaction through the solder-melt temperature range, is required for good performance and thus, good die yield. Finally, this paper briefly highlights three different ways to inspect the solder spheres for proper wetting to the underlying pad and to inspect for polymer residue on the top of the solder spheres. The inspection techniques discussed herein are (1) optical (AOI), (2) SEM, and (3) laser/fluorescence scanning and imaging. This paper briefly discusses the advantages and drawbacks with each inspection method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".