Wafer Map Defect Pattern Classification and Image Retrieval Using Convolutional Neural Network

Abstract

Wafer maps provide important information for engineers in identifying root causes of die failures during semiconductor manufacturing processes. We present a method for wafer map defect pattern classification and image retrieval using convolutional neural networks (CNNs). Twenty eight thousand six hundred synthetic wafer maps for 22 defect classes are generated theoretically and used for CNN training, validation, and testing. The overall classification accuracy for the 6600 test dataset is 98.2%. One thousand one hundred and ninety one real wafer maps are used for CNN performance evaluation for the same model trained by synthetic wafer maps. We demonstrate that by using only synthetic data for network training, real wafer maps can be classified with high accuracy. For image retrieval, a binary code for each wafer map is generated from an output of a fully connected layer with sigmoid activation. A retrieval error rate is 0.36% for the test dataset and 3.7% for the real wafers. Image retrieval takes 0.13 s per wafer map from the 18 000 wafer map library.