Assessment of Delamination Risk During the Package Sawing Process by Simulation
Bibliographic record
Abstract
Delamination at the interface between leadframe and epoxy mold compound (EMC) is a typical concern in microelectronic packaging. Deterioration after moisture penetration can result in lots of quality issues, like Cu wire corrosion during application conditions, die crack or package crack during forming and singulation processes, and Cu wedge crack during thermal cycling test (TCT). Delamination could initiate during the assembly process and propagate during moisture sensitive level (MSL) pre-conditioning, testing or customer application conditions. Among these, package sawing singulation is the key factor that can initiate delamination. Therefore, understanding the stress resulting from sawing will help to drive leadframe design and sawing process optimization to minimize delamination concerns. Numerical modeling can play a vital role in addressing the challenges; especially in predicting differences on stress for delamination with different sawing process parameters and leadframe designs. In this study, simulations are conducted using Ansys Explicit Dynamics with the blade tip modelled and half of a unit that is next to saw street attached to adhesive film. Different sawing parameters with sawing blade starting position nominal vs offset, sawing blade move down speed, and sawing blade rotational speed are studied for an optimal process window to minimize delamination. Different leadframe designs are also studied to identify the best design to minimize delamination risk during the sawing process. Simulation results show lower stress for a lower blade down speed and lower blade rotation speed, indicating lower delamination risk. While a leadframe design that use less Cu and more EMC within the saw street can also reduce delamination risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".