Defect detection and classification on semiconductor wafers using two-stage geometric transformation-based data augmentation and SqueezeNet lightweight convolutional neural network

The manufacturing industry is evolving in line with the principles of Industry 4.0, with the aim of achieving higher levels of automation and digitization. In particular, deep learning algorithms such as convolutional neural networks (CNNs) are key enabling technologies to achieve this goal. The semiconductor industry is a particular case where CNNs are used to assist inspection systems and human operators in defect classification of wafers. However, deep CNN models are time consuming and resource intensive. It is therefore necessary to look for alternatives. One of these alternatives is the use of lightweight models, which provide competitive classification performance with low time and resource consumption. Therefore, the motivation of this work is to apply these lightweight models to semiconductor defect classification and compare their performance with that obtained by deep CNN models in similar work. In this line, this paper introduces an efficient two-step approach combining traditional computer vision techniques and a lightweight SqueezeNet CNN for defect detection and classification. The lightweight SqueezeNet model is tuned using a grid search algorithm. After obtaining the optimal model, its metrics are presented and compared with results from related work. Using a semiconductor surface defect dataset from a multinational semiconductor company, our lightweight model can achieve really competitive classification results (99.356% versus 99.443% obtained by ResNet50) while consuming significantly less time than other heavyweight models (80.146% less time than ResNet50).