A manufacturing approach to reducing underfill voiding on large die (> 18 mm) flip chip organic laminate packaging
Bibliographic record
Abstract
The rising production levels on flip chip large dies (15 to 26 mm square) and low stand off solder C4's has brought to light greater process sensitivity with respect to the formation of voids in underfills. The formation of voids is a result of flow limitations and / or moisture diffusion from the laminate during the capillary underfill dispense and cure processes. Voiding propensity depends on a number of factors, such as underfill type and multiple variables from the laminate and chip construction and designs. Another set of factors however are controllable through process, and include preparatory bakes, preheat steps, adequate temperature control during the dispense process as well as underfill dispense pattern strategy. This paper summarizes a series of experiments conducted to better understand the interplay of several of these factors and the validation of best practices through controlled experiments on the production line. The diffusion behaviour of moisture in laminates can be roughly anticipated with standard models of moisture diffusion in epoxy and offers insights to proper adjustments of preparatory thermal treatments. The data indicates that appropriate thermal treatments to, minimise the possibility of moisture absorption and a good control of dispense parameters are key elements to reducing the occurrence of voids in underfills.
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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) | 0.001 | 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".