A Bayesian approach to managing learning-curve uncertainty
Management Science
An online collaborative semiconductor yield forecasting system
Expert Systems with Applications: An International Journal
Evaluating and Enhancing the Long-Term Competitiveness of a Semiconductor Product
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Computers and Industrial Engineering
A hybrid fuzzy and neural approach for DRAM price forecasting
Computers in Industry
Knowledge management in competitive control of the machining systems
ICOSSSE'10 Proceedings of the 9th WSEAS international conference on System science and simulation in engineering
Forecasting the yield of a semiconductor product with a collaborative intelligence approach
Applied Soft Computing
A flexible way of modeling the long-term cost competitiveness of a semiconductor product
Robotics and Computer-Integrated Manufacturing
Modelling the Long-Term Cost Competitiveness of a Semiconductor Product with a Fuzzy Approach
International Journal of Fuzzy System Applications
A fuzzy-neural approach for global CO2 concentration forecasting
Intelligent Data Analysis
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Yield is undoubtedly the most critical factor to the competitiveness of a product in a semiconductor fabrication plant. Therefore, evaluating the competitiveness of a product with its yield is a reasonable idea. For this purpose, a systematic procedure is established to evaluate the mid-term competitiveness of a product based on the yield learning model. Further, a new correction function is designed to incorporate expert opinions about the mid-term yield target to main the competitiveness of a product into Chen and Wang's fuzzy yield learning model. Such expert opinions are very valuable to controlling the yield learning process and have not been considered in traditional models. The modified model ought to be more practical and accurate than the original one. To evaluate the advantages or disadvantages of the proposed methodology, it is applied to the practical data of four products. Experimental results show that the proposed model outperforms the other models by reducing MAPE to only 2%. Besides, as the evaluated competitiveness decreases, the superiority of the proposed model becomes more evident.