Yunxia Ji
Papers
1
Total Citations
2
H-Index
1
About
Yunxia Ji is a leading researcher at the intersection of semiconductor manufacturing and intelligent automation, with a primary focus on predictive maintenance and advanced machine learning for precision robotics. Her most-cited work, "Advanced Machine Learning Techniques for Predicting Z-Axis Belt Wear in Wafer Transfer Robots" (2025), addresses a critical bottleneck in semiconductor fabrication: the degradation of Z-axis belts in wafer-transfer robots (WTRs). Ji’s key contribution lies in developing sophisticated ML models that can forecast belt wear before it leads to costly misalignments, operational disruptions, or defects—directly enhancing production yield and equipment uptime. With 2 citations in its first year, this paper is already gaining traction as a foundational reference for condition-based monitoring in high-precision manufacturing. Ji’s research is notable for bridging theoretical machine learning with real-world industrial challenges, offering practical solutions that reduce downtime and maintenance costs. Her work is essential reading for engineers and researchers in semiconductor automation, predictive analytics, and robotics, positioning her as a rising authority in smart manufacturing and equipment health prognostics.
Research Focus
Key Achievements
Top Papers
- 1