Abstract
The complexity of semiconductor manufacturing is increasing due to the smaller feature sizes, greater number of layers, and existing process reentry characteristics. As a result, it is difficult to manage and clarify responsibility for low yields in specific products. This paper presents a comprehensive data mining method for predicting and classifying the product yields in semiconductor manufacturing processes. A genetic programming (GP) approach, capable of constructing a yield prediction system and performing automatic discovery of the significant factors that might cause low yield, is presented. Comparison with the results then is performed using a decision tree induction algorithm. Moreover, this research illustrates the robustness and effectiveness of this method using a well-known DRAM fab’s real data set, with discussion of the results.
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Received: November 2004 / Accepted: September 2005
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Li, TS., Huang, CL. & Wu, ZY. Data Mining using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System. J Intell Manuf 17, 355–361 (2006). https://doi.org/10.1007/s10845-005-0008-7
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DOI: https://doi.org/10.1007/s10845-005-0008-7