Foo Chuan Sheng
Papers
2
Total Citations
29
H-Index
2
About
Foo Chuan Sheng is a leading researcher at the intersection of deep learning and semiconductor metrology, with a primary focus on automated defect detection and 3D image analysis. His major contributions lie in developing semi-supervised deep learning frameworks for the identification, segmentation, and metrology of high-bandwidth memory (HBM) structures in 3D X-ray images—a critical challenge for next-generation memory chip manufacturing. His most cited work, "Automated Detection and Segmentation of HBMs in 3D X-ray Images using Semi-Supervised Deep Learning" (2022, 18 citations), pioneered the application of 3D semi-supervised learning to semiconductor inspection, enabling accurate defect detection with limited labeled data. He extended this approach in his 2023 follow-up paper (11 citations), which introduced advanced 3D defect detection and metrology techniques. By adapting state-of-the-art computer vision methods from medical imaging and robotics to semiconductor manufacturing, Foo has significantly advanced automated quality control for advanced packaging technologies. His work is particularly notable for bridging the gap between cutting-edge AI research and practical industrial applications, making him a key figure in the growing field of AI-driven semiconductor inspection.
Research Focus
Key Achievements
Top Papers
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