Redefining Monitoring Rules for Intelligent Fault Detection and Classification via CNN Transfer Learning for Smart Manufacturing

Fault detection and classification has been employed to enhance yield and product quality for smart semiconductor manufacturing. For early detection of abnormal events that cause defects, the status variables identification data collected by the sensors embedded in advanced machines can be analyzed to derive the actions for advanced process control and advanced equipment control. However, the validity and effectiveness of fault detection and classification technologies may highly depend on domain knowledge and experience of the process engineers who should redefine the monitoring rules quickly when new process excursion occurred especially when ramping up new technologies and products. Motivated by realistic needs, this study aims to propose a novel strategy to empower intelligent fault detection and classification that employed convolutional neural network to analyze the feature SVID data and determine the conditions of the wafers, while shorten the cycle time for self-learning from domain knowledge and redefining new monitoring rules for fault classification in real time. This approach is validated with an empirical study in a leading semiconductor manufacturing company in Taiwan. The results have demonstrated the practical viability of the developed solution.

Automated summary

Fault detection and classification play a crucial role in smart semiconductor manufacturing. This study proposes a novel strategy using convolutional neural network to analyze feature data, shorten the cycle time for self-learning, and redefine monitoring rules in real-time, enhancing intelligent fault detection and classification.