Predictive Monitoring in Semiconductor Manufacturing
Develop a software solution to monitor semiconductor equipment machine health and extend the preventative maintenance schedule
Extended component life by 25% in just the first six months of the analysis project, significantly reducing component replacement and labor cost
A global manufacturer of industrial equipment, electronics components and consumer technologies was looking to extend the preventative maintenance schedule for their semiconductor equipment. At the time, their more conservative timetable for maintenance was causing unnecessary component replacement and excess labor cost.
Predictronics created a baseline health index and a prediction model from various sensor measurements to predict the life and health of components within the etching equipment. The Kalman filter prediction method was utilized to reduce the noise and uncertainty that can occur from using multiple different sensors. Employing this prediction method ensures the manufacturer has the most accurate health and prediction estimates from their sensor data.
By using the developed index model, this business can now extend the health of the chamber’s components within their dry-etching equipment by 25%. The semiconductor manufacturer is currently in the process of deploying their solution further. Over time, the model will become more accurate as it is fine-tuned and validated with additional data from the semiconductor fabrication plant. This data-driven solution will allow for a more efficient maintenance schedule, saving the company wasted time, component resources and labor cost.