Markov-chain based missing value estimation method for tool commonality analysis in semiconductor manufacturing

Association rule-based tool commonality analysis (ARBTCA) is an effective approach to identifying tool excursions for yield enhancement in semiconductor manufacturing. However, missing values which frequently occurred will lead to high rates of false positive and false negative. Incorrect identification of root cause of yield loss will lose engineer´s trust on TCA and delay the process improvement opportunity. In, this paper, we proposed a Markov-chain based Missing Value Estimation (MCBMVE) method to improve the effectiveness of ARBTCA, and demonstrate and explain why traditional methods dealing with missing values for association rules cannot solve the problem. Comparing with traditional methods, the real case study shows that MCBMVE is more accurate in recovering missing values so as to improve the identification accuracy.