Chong Ser Choong
Papers
2
Total Citations
29
H-Index
2
About
Chong Ser Choong is at the forefront of applying advanced deep learning techniques to semiconductor inspection and metrology. His research centers on the automated detection, segmentation, and metrology of high-bandwidth memory (HBM) structures in three-dimensional X-ray images—a critical challenge for next-generation chip manufacturing. In his highly cited 2022 work, Choong pioneered a semi-supervised deep learning framework for automated detection and segmentation of HBMs, achieving robust performance with limited labeled data. This breakthrough has garnered 18 citations and established a new paradigm for defect detection in voxelized semiconductor data. Building on this foundation, his 2023 study extended the approach to full 3D defect detection and metrology, leveraging recent advances in 3D semi-supervised learning to deliver precise, scalable inspection solutions. With a combined 29 citations from these two foundational papers, Choong’s contributions bridge the gap between cutting-edge computer vision and practical semiconductor manufacturing, offering transformative tools for quality control in advanced packaging. His work stands as a vital reference for researchers and engineers seeking to harness deep learning for industrial 3D imaging challenges.
Research Focus
Key Achievements
Top Papers
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- 2