Continued advances in machine learning (ML) and deep learning (DL) present new opportunities for use in a wide range of applications. One prominent application of these technologies is defect detection and classification in the manufacturing industry in order to minimise costs and ensure customer satisfaction. Specifically, this scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL techniques and configurations have been used for defect detection and classification. Inspection operations have traditionally been carried out by specialised personnel in charge of visually judging the images obtained with a scanning electron microscope (SEM). This scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL methods have been used to detect and classify defects in SEM images. We also include the performance results of the different techniques and configurations described in the articles found. A thorough comparison of these results will help us to find the best solutions for future research related to the subject.

Semiconductors are essential components in many electronic devices. Because wafers are produced quickly and in large quantities, defects occur that adversely affect semiconductor properties. This makes it necessary to install powerful and robust inspection systems which use artificial intelligence techniques in the early stages of the manufacturing chain in order to detect and classify those defects. This paper proposes a method for defect detection and classification on images of semiconductor wafer materials obtained by means of a scanning electron microscope based in the following stages: (i) use of computer vision techniques to isolate the defect from the background; (ii) use of several descriptors based on shape, size, texture, histogram, and key-points to create a feature vector for the characterization of the defect; (iii) application of an exhaustive search as a feature selection method to determine the optimal subset of feature descriptors; and (iv) evaluation of the feature descriptors by using a support vector machine classifier providing the optimal set with highest F1-score metrics. Finally, the effectiveness of the proposed approach is compared with five popular feature selection methods, reporting better classification results than the latter.