A novel design of wafer yield model for semiconductor using a GMDH polynomial and principal component analysis

According to previous studies, the Poisson model and negative binomial model could not accurately estimate the wafer yield. Numerous mathematical models proposed in past years were very complicated. Furthermore, other neural networks models can not provide a certain equation for managers to use. Thus, a novel design of this paper is to construct a new wafer yield model with a handy polynomial by using group method of data handling (GMDH). In addition to defect cluster index (CIM), 12 critical electrical test parameters are also considered simultaneously. Because the number of input variables for GMDH is inadvisable to be too many, principal component analysis (PCA) is used to reduce the dimensions of 12 critical electrical test parameters to a manageable few without much loss of information. The proposed approach is validated by a case obtained in a DRAM company in Taiwan.