Papers
2
Total Citations
36
H-Index
2
About
Oo Zaw Min is a researcher at the forefront of applying deep learning to industrial imaging and non-destructive inspection. His primary research areas include automated object detection, 3D image segmentation, and semi-supervised learning for X-ray analysis. Min’s major contributions lie in developing state-of-the-art deep learning models to detect and segment buried structures—such as through-hole components and high-density buried modules (HBMs)—in complex 3D X-ray volumes. His work bridges the gap between advanced computer vision techniques and practical semiconductor inspection, enabling faster, more reliable quality control. With two of his most-cited papers each garnering 18 citations, Min’s research demonstrates clear impact in a specialized but critical field. Notably, his 2021 and 2022 studies showcase how semi-supervised and fully supervised deep learning can automate attribute measurements and defect detection in voxelized data, a task traditionally reliant on manual or rule-based methods. By adapting techniques from robotics and medical imaging to industrial challenges, Min is helping to drive the next generation of intelligent inspection systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2