A Deep Convolutional Neural Network for Wafer Defect Identification on an Imbalanced Dataset in Semiconductor Manufacturing Processes

Abstract

Wafer maps contain information about various defect patterns on the wafer surface and automatic classification of these defects plays a vital role to find their root causes. Semiconductor engineers apply various methods for wafer defect classification such as manual visual inspection or machine learning-based algorithms by manually extracting useful features. However, these methods are unreliable, and their classification performance is also poor. Therefore, this paper proposes a deep learning-based convolutional neural network for automatic wafer defect identification (CNN-WDI). We applied a data augmentation technique to overcome the class-imbalance issue. The proposed model uses convolution layers to extract valuable features instead of manual feature extraction. Moreover, state-of-the-art regularization methods such as batch normalization and spatial dropout are used to improve the classification performance of the CNN-WDI model. The experimental results comparison using a real wafer dataset shows that our model outperformed all previously proposed machine learning-based wafer defect classification models. The average classification accuracy of the CNN-WDI model with nine different wafer map defects is 96.2%, which is an increment of 6.4% from the last highest average accuracy using the same dataset.