Olukoya, Oluwawunmi (2025) The role of surface topology on reactive wetting of SAC305 solder on Ag substrates. PhD thesis, University of Sheffield.
Abstract
Physical removal of material via grit paper is the most common method of preparing surfaces for soldering. Most researchers have shown that increased roughness improves solder spreading; however, what isn't fully known is which exact feature or features create the positive outcome. This research was primarily focused on enhancing SAC305 solder interconnects to silver(Ag) via surface modification.
Ag substrates were roughened using grit paper (P120-P1000). Except for P1000, all surfaces had a final area of 2.23 +/- 0.25 mm^2, indicating minimal correlation between surface roughness and solder spreading.
A femtosecond laser was used to create parallel and hatched Ag substrates with various hatch distances and angles. The final spreading area for all the solder samples for all parallel substrates was 2.5 +/- 1 mm^2, showing no correlation between the final spreading area and hatch spacing. In contrast, altering the hatch angle resulted in minimal improvement of the mean final spreading area, of 3 +/- 1 mm^2. The exception was the 90-degree cross-hatched sample with a final spreading area equivalent to the parallel surfaces. The optimal hatch angle was 115 degrees, cross-hatched with a final solder spreading area of 3.2 mm^2. Laser-textured substrates were shown to have some control over the molten solder flow direction and morphology. This may allow improved joints and greater control of joint placement.
Otsu and Multi-Otsu methods were applied to SEM images to analyse solder/Ag interfaces. These methods identified interfaces but struggled to differentiate Ag and Cu due to minimal atomic number differences. BSE imaging provided a topological map, isolating the harder intermetallic compounds of Ag3Sn and Cu6Sn5 within the solder matrix. However, there was some uncertainty, leading to manual image adjustments that made analysis subjective and time-consuming.
A Mask R-CNN model, developed using Detectron2 on hot-stage microscopy images of SAC305 solder on Ag substrates. The model achieved 99% accuracy and an average precision of 76.25%
Metadata
| Supervisors: | Goodall, Russell |
|---|---|
| Keywords: | SAC305, Silver, Ag, Lead-free solder, Laser-Texturing, Machine Learning, Detectron2, Mask R-CNN, Surface Roughness |
| Awarding institution: | University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Materials Science and Engineering (Sheffield) |
| Date Deposited: | 17 Nov 2025 10:39 |
| Last Modified: | 17 Nov 2025 10:39 |
| Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:37744 |
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