To improve prediction accuracy of die yield, a novel fuzzy neural networks (FNN) based yield prediction approach is proposed. The yield prediction model is built, in which the impact factors of yield, including physical parameters, electrical test parameters and wafer defect parameters are considered simultaneously and are taken as independent variables. A back-propagation algorithm is used to train and adjust the weight parameters and variables of fuzzy membership functions. By historical experimental data of wafer fabrication line in shanghai, the comparison experiment shows that the FNN prediction model can get better precision than the Poisson model, the negative binomial model and neural network model.
['Lihui Wu', 'Jie Zhang', 'Gong Zhang']
A Fuzzy Neural Network Approach for Die Yield Prediction of Wafer Fabrication Line