| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
The 7th International Congress on 3D Materials Science (3DMS 2025)
|
| Presentation Title |
Comparative Analysis of Reconstruction Methods for Lab-Scale X-Ray Computed Tomography of 3D Defects in Semiconductor Packages |
| Author(s) |
Eshan Ganju, Yaw Obeng, William Harris, Charles Bouman, Gregery T Buzzard, Nikhilesh Chawla |
| On-Site Speaker (Planned) |
Nikhilesh Chawla |
| Abstract Scope |
Rapid co-design of semiconductor packages necessitates an efficient and non-destructive metrology to detect defects. Lab-scale X-ray Computed Tomography systems, while valuable, face challenges such as long scan times due to limited x-ray flux and imaging artifacts arising from density variations and reconstruction methods. This study critically evaluates three distinct reconstruction techniques for lab-scale XCT data: Filtered Back Projection (FBP), Model-Based Iterative Reconstruction (MBIR), and Deep Learning (DL) approaches. FBP serves as the baseline for comparison. MBIR leverages physical models and noise statistics to enhance image quality and potentially reduce scan times. DL methods, trained on extensive datasets, offer a data-driven approach to reconstruction. Reconstruction performance was assessed through quantitative metrics, scan and reconstruction time, and signal-to-noise ratio. The advantages and limitations of each method are discussed in detail, supplemented by visual assessments of exemplars in semiconductor packages, to provide insights into their suitability for high-fidelity lab-scale XCT semiconductor packaging applications. |
| Proceedings Inclusion? |
Undecided |