Advanced Techniques in Semiconductor Defect Detection and Classification: Overview of Current Technologies and Future Trends in AI/ML Integration

This review evaluates advancements and future trends in semiconductor defect detection methods, which are critical for enhancing electronic components' efficiency and reliability. As semiconductor devices shrink and become more complex, the accuracy of defect detection becomes crucial. This paper traces the evolution from manual inspections to the use of advanced technologies such as automated vision systems, artificial intelligence (AI), and machine learning (ML). It discusses various defects like crystallographic errors, surface anomalies, and chemical impurities that affect device functionality and longevity, emphasizing the need for precise identification. The shift to ML and deep learning (DL) represents a significant move towards more adaptive, accurate, and faster detection methods. The paper outlines challenges like the miniature scale of modern devices, high costs of advanced imaging technologies, and the need speed in mass production. It identifies a critical gap between current technological capabilities and industry needs, particularly in scalability and processing throughput. Future research directions are suggested to close these gaps, including enhancing AI computational efficiency, developing new materials for better imaging contrast, and integrating these technologies seamlessly into production lines. This synthesis of current technologies and exploration of future trends aims to advance the dialogue and development of more effective defect detection and classification methods, leading to the production of more reliable semiconductor devices.