Combined Surface Activated Bonding Technique for Hydrophilic SiO<sub>2</sub>-SiO<sub>2</sub> and Cu-Cu Bonding
Bibliographic record
Abstract
As an evolution of the Cu-Cu bonding and SiO 2 -SiO 2 bonding, Cu/SiO 2 hybrid bonding is a promising approach to the emerging three-dimensional (3D) integration of microelectronic/photonic systems, since it obtains both direct metal interconnection and enhanced thermal/mechanical stability with a seamless bonding structure during the single bonding process. 1,2 However, because of the different features of Cu-Cu and SiO 2 -SiO 2 bonding, Cu/SiO 2 hybrid bonding at low temperatures of no more than 200 °C remains challenging. For instance, the Cu-Cu thermo-compression bonding is typically conducted in vacuum or dry protecting/reducing atmospheres after removal of surface oxides, 3–7 while the SiO 2 -SiO 2 bonding needs a humid environment to facilitate termination of Si-OH bonding sites. 8–12 It is highly desired to develop a new bonding process that is effective for both Cu-Cu and SiO 2 -SiO 2 bonding in H 2 O-free ambient, such as vacuum, for improvement of the Cu/SiO 2 hybrid bonding. Recently, we proposed a combined surface-activated bonding (SAB) method, which involves a combination of surface bombardment using a Si-containing Ar beam and prebonding attach-detach procedure prior to bonding in vacuum. High SiO 2 -SiO 2 bonding strength of close to the Si bulk fracture strength has been realized at 200 °C. In this paper, we report our recent results of Cu-Cu and SiO 2 -SiO 2 bonding by using the combined SAB method. The mechanism is discussed to understand the present low-temperature bonding technique. References 1. L. D. Cioccio et al., J. Electrochem. Soc. , 158 , P81–P86 (2011). 2. H. Moriceau et al., Microelectron. Reliab. , 52 , 331–341 (2012). 3. W. Yang, M. Akaike, M. Fujino, and T. Suga, ECS J. Solid State Sci. Technol. , 2 , P271–P274 (2013). 4. W. Yang, M. Akaike, and T. Suga, IEEE Trans. Compon. Packag. Manuf. Technol. , 4 , 951–956 (2014). 5. B. Rebhan and K. Hingerl, J. Appl. Phys. , 118 , 135301 (2015). 6. T. H. Kim, M. M. R. Howlader, T. Itoh, and T. Suga, J. Vac. Sci. Technol. A , 21 , 449–453 (2003). 7. A. Shigetou, T. Itoh, K. Sawada, and T. Suga, IEEE Trans. Adv. Packag. , 31 , 473–478 (2008). 8. Q.-Y. Tong and U. M. Gösele, Adv. Mater. , 11 , 1409–1425 (1999). 9. T. Suni, K. Henttinen, I. Suni, and J. Mäkinen, J. Electrochem. Soc. , 149 , G348–G351 (2002). 10. F. Fournel et al., ECS J. Solid State Sci. Technol. , 4 , P124–P130 (2015). 11. H. Takagi, J. Utsumi, M. Takahashi, and R. Maeda, ECS Trans. , 16 , 531–537 (2008). 12. R. He, M. Fujino, A. Yamauchi, and T. Suga, Jpn. J. Appl. Phys. , 54 , 030218 (2015).
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".