Low Temperature Bonding of Cu Metal through Sintering of Ag Nanoparticles for High Temperature Electronic Application
Bibliographic record
Abstract
Lead-based solders bring pollution to the environment and result in health threat to humans. The preparation and application of metallic nanoparticles provide a potential method to develop Pb-free bonding materials. In this article, bonding of Ag-coated Cu bulks was realized through low temperature sintering by directly using the chemically-reduced Ag nanoparticle paste and baked nanoparticle powders at 60 o C, respectively. The results indicate that the capillary flow of paste caused a ring-like deposit on substrate coating, while this phenomenon disappeared when using the powders. Increasing bonding temperature facilitated the sintering, and shear strengths of 20 MPa and 84 MPa was obtained at bonding temperature of 250 o C for 30 min under 20 MPa when using the Ag paste and powders, respectively. High joint strength value of bonded region using paste is due to the high effective bonding pressure and small effective bonded area. Finally, for the extensive application in packaging industry especially for high temperature electronics, challenges such as the improvement of screen and stencil printing ability of paste, avoiding bonding pressure and lowering the cost were pointed out based on our and other researcher's achievements. Cu nanoparticle or Cu-containing nanoparticle mixture pastes are promising but the problems of oxidation and bonding stability must be resolved urgently.
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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".