Edgebond and Edgefill Induced Loading Effect on Large WLCSP Thermal Cycling Performance
Bibliographic record
Abstract
ABSTRACT Various external load conditions affecting components on electronic devices and modules are constant factors, which need to be considered for the component long-term reliability. Recently, to enhance the high stress component thermo-mechanical cycling performance, various types and configuration using edgebond and edgefill technology are introduced and tested. These applications induce a multi-axis loading condition, which alter the degradation mechanism and failure location during thermal cycling, which need closer investigation. In this study, high stress 12×12mm 2 wafer level chip scale packages (WLCSP) were selected and subject to thermal cycling with full-edgebond, dot-edgebond and edgefill adhesive, which improves the characteristic lifecycle numbers base on the configurations, but altered the failure location due to different stress conditions. The −40 to 125°C thermal cycling profile revealed localized degradation per configuration during thermal cycling, showed a shift of the crack propagation path, based on full-edgebond, dot-edgebond and edgefill adhesive sample conditions. Through these series of observation, the interconnect thermal cycling degradation mechanisms are able to be explained. The correlation between the stress condition and microstructure are presented and discussed based on Electron backscattered diffraction (EBSD) analysis.
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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 | 0.000 |
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".