Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
A Unified Negative-Binomial Distribution for Yield Analysis of Defect-Tolerant Circuits
IEEE Transactions on Computers
A Statistical Parametric and Probe Yield Analysis Methodology
DFT '96 Proceedings of the 1996 Workshop on Defect and Fault-Tolerance in VLSI Systems
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Fuzzy c-means clustering methods for symbolic interval data
Pattern Recognition Letters
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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.