Sang Jeen Hong

About

Sang Jeen Hong is a leading researcher in semiconductor manufacturing equipment intelligence, specializing in machine learning-driven predictive maintenance and fault diagnosis for critical wafer-handling systems. His work focuses on enhancing the reliability of wafer transfer robots (WTRs), which are essential for productivity in semiconductor fabs. Hong’s major contributions include developing novel frameworks that integrate Gaussian mixture models, mean-shift clustering, and explainable artificial intelligence (XAI) to detect, predict, and diagnose component failures in real time. By defining high-risk components such as bearing motors, ball screws, and end effectors, he has enabled targeted condition-based monitoring using acceleration sensor data and fast Fourier transformation. His 2024 studies, including those on fault prediction and predictive maintenance, have collectively garnered over 15 citations, reflecting growing industry and academic interest. Hong’s work not only minimizes costly downtime but also advances transparent AI methods for root cause identification, making his research highly impactful for both semiconductor manufacturing and the broader field of industrial equipment maintenance.

Research Focus

Key Achievements

Top Papers

1. 1
   Utilization of Machine Learning and Explainable Artificial Intelligence (XAI) for Fault Prediction and Diagnosis in Wafer Transfer Robot

   7 citations · 2024
2. 2
   Predictive Maintenance System for Wafer Transfer Robot Using Gaussian Mixture Model and Mean-Shift Clustering

   6 citations · 2024
3. 3
   Condition-Based Monitoring for Failure Detection and Cause Identification in Wafer Transfer Robot Using Machine Learning and Explainable Artificial Intelligence Algorithms

   2 citations · 2024

Key Collaborators

Contact & Links