Double bagging trees with weighted sampling for predictive maintenance and management of etching equipment

Abstract

Proper maintenance and management of equipment are essential for producing high-quality wafers. Anomalies in equipment lead to the production of low-quality wafers. This study proposes a method to maintain and manage etching equipment in semiconductor manufacturing utilizing a virtual metrology (VM) model. Leveraging acquired equipment data, the VM model predicts electrical resistance measurement values to monitor the equipment state. Engineers determine the equipment state by inspecting the electrical resistance values and consistency of variance in the measurement data derived from the VM model. However, conventional complex machine learning models frequently yield predicted values with limited variability, making it challenging to detect abnormal equipment states. To address this issue, we propose a novel method, double bagging trees with weighted sampling, which guarantees the predicted values follow a proper distribution and exhibit a variance that aligns with the actual measurement values. The proposed method provides reliable predictions about the equipment state. A case study utilizing a real-world semiconductor manufacturing dataset is presented to demonstrate the effectiveness of the proposed approach. The VM model provides timely information about the state of equipment, which helps engineers maintain and manage equipment efficiently.