A yield diagnosis method basedon random forest for addressable WAT

WAT(Wafer Acceptance Test) is an electrical test that a wafer must pass upon completion of fabrication. An add ressable WATyield diagnosis method is proposed in this paper, which uses the random forest algorithm to establish a classification model on test data and extractskey rules.The extracted rules can help analysts quickly and accurately locate the rootcause of low yield in production, which plays a key role in yield enhancement. The proposed method hasbeen experimented on dataset from arealaddressable WAT, and the obtained rule set has better classification performance than the rule set obtained by method based on decision tree.