Fuzzy neural network based yield prediction model for semiconductor manufacturing system

Abstract

Accurate die yield prediction is very useful for improving yield, decreasing cost and maintaining good relationships with customers in the semiconductor manufacturing industry. To improve prediction accuracy of die yield, a novel fuzzy neural networks based yield prediction model is proposed in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The mapping between these independent variables and yield is constructed in the fuzzy neural network (FNN). The lineal regression between FNN-based yield predicting output and actual yield demonstrates the effectiveness of the proposed approach by historical experimental data of semiconductor fabrication line in Shanghai. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy.

Keywords:

• semiconductor manufacturing system
• yield
• fuzzy neural networks
• prediction

Acknowledgements

This work was supported by the National Nature Science Foundation of China under Grant No. 50575137, by the National High Tech R & D Program under Grant No. 2007AA04Z109, and by State Key Laboratory of Digital Manufacturing Equipment and Technology at Huazhong University of Science and Technology, China.