Data Mining using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System

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. Received: November 2004 / Accepted: September 2005