Yudan Wang
Papers
1
Total Citations
85
H-Index
1
About
Yudan Wang’s research lies at the intersection of advanced manufacturing, robotics, and artificial intelligence, with a primary focus on quality inspection for semiconductor and industrial automation systems. Her most cited work, “A Novel Method Based on Deep Convolutional Neural Networks for Wafer Semiconductor Surface Defect Inspection” (2020, 85 citations), introduces a deep learning approach to detect critical surface defects—including stains, burrs, scratches, and holes—that compromise the performance of semiconductor wafers used in welding robots, spray robots, unmanned material delivery vehicles, and detection station sensors. By leveraging convolutional neural networks, Wang’s method significantly improves defect detection accuracy and speed, directly addressing a bottleneck in downstream product quality. This contribution is particularly impactful for smart manufacturing environments where real-time, automated inspection is essential. Wang’s work bridges the gap between computer vision and industrial robotics, offering practical solutions for quality control in high-precision production lines. Her research continues to influence the development of intelligent inspection systems, making her a key figure in the advancement of AI-driven manufacturing and semiconductor process optimization.
Research Focus
Key Achievements
Top Papers
- 1