Improvement of SLID Bonding for Ni/Ni Joints in 3D-IC Packages and Power Modules through Ag3Sn Intermetallic Interlayer
Bibliographic record
Abstract
Solid liquid interdiffusion bonding (SLID) provides a promising technique for advanced packages.However, the uneven growth of intermetallic compounds (IMCs) in Ni/Ni joints often causes the appearance of a large number of voids, which degrade the bonding strength and electrical and thermal conductivities.An example of the application in the Si/Si wafer bonding of 3D-IC packages was shown in the SLID bonding of Ni/Sn/Ni between two Si substrates at 250 °C for 30 min, which resulted in the formation of many voids at the interface between Ni3Sn4 intermetallic compound layers and a shear strength of 13 MPa.An improved method through the employment of an innovative patented concept of dual-phase IMCs has been proposed to diminish the voids and increase the bonding strength.This technique uses an additional 2μm Ag thin film to be inserted into the Ni/Sn/Ni sandwich to form the Ni/Ag/Sn/Ni or Ni/Sn/Ag/Sn/Ni metal stacks.After SLID bonding, the interfacial reactions between Ag and Sn result in the appearance of Ag3Sn IMCs filling the voids of the simultaneously formed scallop-like Ni3Sn4 IMCs to achieve a void-free interconnection.The average shear strength was drastically increased to 20 MPa.A similar example for the application in die attachment of power modules was also shown in the SLID bonding of Si/Ti/Ni/Ag/Sn with Au/Pd/Ni/Cu/Al2O3 DBC substrates at temperatures from 250 °C to 350 °C for 30 min to eliminate the interfacial voids and also obviously increase the bonding strengths.
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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".