A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

• Published in: Applied sciences Vol. 11; no. 20; p. 9508
• Main Authors: López de la Rosa, Francisco, Sánchez-Reolid, Roberto, Gómez-Sirvent, José L., Morales, Rafael, Fernández-Caballero, Antonio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2021
• Subjects: Accuracy; Algorithms; Classification; computer vision; Deep learning; defect classification; Defects; Digital libraries; Efficiency; Machine learning; Manufacturing; scanning electron microscope; Scanning electron microscopy; semiconductor
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11209508

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.