| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
The 7th International Congress on 3D Materials Science (3DMS 2025)
|
| Presentation Title |
Multimodal Microstructural Image Segmentation of Low-Temperature Sn3Ag0.5Cu7Bi Solder Using Multi-Channel Deep Learning |
| Author(s) |
Eshan Ganju, John Wu, Nikhilesh Chawla |
| On-Site Speaker (Planned) |
Eshan Ganju |
| Abstract Scope |
High-throughput and accurate semantic segmentation of electron microscopy data is crucial for efficient microstructural analysis, particularly in complex low-temperature solders. To obtain a complete picture of a materials system, various complementary streams of data are often required. By combining backscattered electron images, secondary electron images, and energy dispersive spectroscopy data as input for a deep learning-based approach, we can effectively exploit the complementary information provided by each imaging modality. When applied to a 3D dataset, this approach capitalizes on the unique material contrast from backscatter, surface topography from secondary electrons, and precise elemental distribution from compositional maps, leveraging the power of a multi-channel Unet++ inspired architecture to improve segmentation accuracy. Evaluation on an Sn3Ag0.5Cu7Bi dataset demonstrated that the multi-channel approach performs well and enables accurate identification and quantification of microstructural features. This study highlights the potential of multi-channel deep learning for advanced characterization of complex microstructures. |
| Proceedings Inclusion? |
Undecided |