Defect Detection on Wafer Map Using Efficient Convolutional Neural Network

In semiconductor manufacturing, defect patterns on wafer maps hide important information about production problems. Therefore, the detection and identification of wafer pattern defects are one of the important topics in semiconductor manufacturing. Since there are few datasets of wafer maps, this paper solves the problem of overfitting by classifying wafer map defects using the model base on MobileNet V2. Finally, the accuracy of the WM-811K dataset reaches 96.56%.