MACHINE LEARNING-BASED PERIODIC SETUP CHANGES FOR SEMICONDUCTOR MANUFACTURING MACHINES

Abstract

Semiconductor manufacturing machines, especially for photo-lithography processes, require large setup times when changing job types. Hence, setup operations do not often occur unless there is no job to be processed. In practice, a simulation-based method that predicts the incoming WIP is often used to determine whether changing machine setup states or not. The simulation-based method can provide useful information on the future production environment with a high accuracy but takes a long time, which can delay the setup change decisions. Therefore, this work proposes a machine learning-based approach that determines setup states of the machines. The proposed method shows better performance than several heuristic rules in terms of movement.