Low temperature sintering-bonding using mixed Cu+Ag nanoparticle paste for packaging application
Bibliographic record
Abstract
In this study,the two kinds of nanoparticle pastes were prepared by using the modified polyol chemical reduction process,respectively,and the mixed nanoparticle paste was prepared by mechanically mixing of the two pastes.The sintering characteristics of the mixed paste were investigated,and the results show that the mixed paste had good oxidation resistance.Ag-coated copper bulks were bonded through the sintering of mixed paste.The results indicate that the increasing of Ag content in the paste was beneficial to high quality of joints,and with the bonding conditions of sintering at 250 ℃ for 5 min under 5 MPa pressure,the average shear strengths of joints using nano-Cu paste,mixed paste consisting of 50% mole Ag and 50% mole Cu,and nano-Ag paste,were 15 MPa,22 MPa and 56 MPa,respectively.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".