Effect of High Glass Transition Temperature on Reliability of Non-Conductive Film (NCF)
Bibliographic record
Abstract
Among many factors that influence the reliability of a flip-chip assembly using NCF interconnections, the most effective parameters are often the coefficient of thermal expansion (CTE), the modulus (E), and the glass transition temperatures (Tg). Of these factors, the effect of Tg on thermal deformation and device reliability is significant; however, it has not been shown clearly what effect Tg has on the reliability of NCF. The Tg of a conventional NCF material is approximately 110°C. In this study, a new high Tg NCF material that has a 140oC Tg is proposed. The thermal behaviors of the conventional and new NCFs between -40oC to 150oC are observed using an optical method. Twyman-Green interferometry and the moiré interferometry method are used to measure the thermal micro-deformations. The Twyman-Green interferometry measurement technique is applied to verify the stress-free state. The stress-free temperatures of the conventional and new Tg NCF materials are approximately 100oC and 120oC respectively. A shear strain at a part of the NCF chip edge is measured by moiré interferometry. Additionally, a method to accurately measure the residual warpage and shear strain at room temperature is proposed. Through the analysis of the relationship between the warpage and the shear strain, the effect of the high-Tg NCF material on the reliability is studied.
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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 | 0.001 |
| 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 | 0.000 | 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".