An Improved XGBoost Prediction Model for Multi-Batch Wafer Yield in Semiconductor Manufacturing

Abstract

Wafer yield is one of the most critical indicators of product quality in semiconductor manufacturing companies. An accurate prediction can reduce the subsequent inspection process, improve production efficiency and reduce product scrap. However, existing end-to-end data-driven wafer yield prediction methods ignore the batch nature of the wafer production process, resulting in poor generalization of the prediction model in different batch wafers. Therefore, an improved XGBoost-based multi-batch wafer yield prediction model is proposed to address this problem. A multi-task learning mechanism is designed to realize the extraction of batch features, and a fusion training mechanism is established to realize the prediction output. Finally, the effectiveness of the prediction method is verified by experimental data collected from the real wafer fabrication process.