A deep residual neural network for semiconductor defect classification in imbalanced scanning electron microscope datasets

Abstract

The detection of defects using inspection systems is common in a wide range of corporations such as semiconductor industries. The use of techniques based on deep learning (DL) and, in particular, convolutional neural networks (CNNs), emerges as a powerful tool to classify aesthetic defects and other unwanted anomalies in industrial automation applications marked by their natural complexity and high degree of variability. In this paper, an effective hybrid deep residual neural network approach is presented which merges traditional computer vision techniques to perform an appropriate defect segmentation and a deep residual neural network-grid search-based hyperparameter optimization for defect classification. The proposed model has been compared with other baseline algorithms and with other hybrid methods-based CNNs using different performance metrics such as F1-score, Cohen’s kappa coefficient, confusion matrix and computing time, on imbalanced datasets obtained from scanning electron microscope (SEM) images. The results obtained illustrate that the designed hybrid method provides the best defect classification of defects in semiconductor wafers in terms of F1-score (99.443%) while consuming the least computational time.