A wafer map contains a graphical representation ofthe locations about defect pattern on the semiconductor wafer,which can provide useful information for quality engineers.Various defect patterns occur due to increasing wafer sizes anddecreasing features sizes, which makes it very complex andunreliable process to identify them. In this paper, we propose avoting ensemble classifier with multi-types features to identifywafer map defect patterns in semiconductor manufacturing. Ourresearch contents can be summarized as follows. First, threedistinctive features such as density-, geometry-, and radon-basedfeatures were extracted from raw wafer images. Then, we appliedfour machine learning classifiers namely logistic regression (LR),random forests (RF), gradient boosting machine (GBM), andartificial neural network (ANN), and trained them using extractedfeatures of original data set. Then their results were combinedwith a soft voting ensemble (SVE) technique which assigns higherweights to the classifiers with respect to their prediction accuracy.Consequently, we got performance measures with accuracy,precision, recall, F-measure, and AUC score of 95.8616%,96.9326%, 96.9326%, 96.7124%, and 99.9114%, respectively.These results show that the SVE classifier with proposed multitypes features outperformed regular machine learning-basedclassifiers for wafer maps defect detection.
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