Reliability of high I/O count wafer level packages
Bibliographic record
Abstract
Thermomechanical fatigue and drop performance of wafer level packages (WLP) are two main reliability requirements that prevent the WLPs from scaling up. A comprehensive investigation into the methods that would enable such transition was undertaken. The current qualification involved a package with 5.2 mm times 5.2 mm die and 144 l/Os. The packages were supplied by four vendors who are the leading suppliers of WLPs. One package design was based on the Cu post technology and the other three utilized double polymer layer for enhanced compliance. In addition to standard WLP designs that have been used on smaller packages, a design of experiments (DOE) also included novel designs which aimed at enhancing the package reliability. The main package modifications included a redesigned under bump metallurgy (UBM) structure with changes made to the polymer opening and the shape of the UBM pad. The packages were manufactured with lead-free SAC105 and LF35 solder balls. The second level reliability was assessed by thermal cycling, drop testing and cyclic bend testing, all according to governing JEDEC specifications. The results obtained from the reliability testing allowed for direct comparison between different vendors and also between different package designs from the same vendors. The failures were examined using a dye stain penetrant method and cross-sectional SEM/EDX. The failure modes and Weibull failure statistics are presented with the emphasis on the qualitative understanding of the results. Recommendations as to the preferred package type, solder ball alloy and package design are also included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".