Image-Based Chip Defect Detection

Abstract

This paper reviews the research status, key technologies, and application prospects of image-based chip defect detection techniques. With the advancement of chip manufacturing processes, traditional inspection methods face challenges in terms of accuracy, efficiency, and automation. Deep learning algorithms (such as YOLO, Faster R-CNN, SSD, etc.) have provided new solutions for defect detection. This paper analyzes the advantages and disadvantages of these algorithms and summarizes improved algorithms (such as enhanced Canny algorithms, multi-scale feature fusion networks, etc.) to address challenges like tiny defects and complex background interference. Meanwhile, the paper examines four major challenges in chip defect detection: the trade-off between high precision and cost, the balance between detection speed and efficiency, the scarcity and imbalanced distribution of defect samples, and insufficient standardization. To address these challenges, technical solutions such as transfer learning and generative adversarial networks (GANs) are proposed, while the importance of algorithm optimization and hardware acceleration is emphasized. Here, fully automated inspection systems, multimodal fusion technologies, and the widespread adoption of AI platforms will become mainstream, driving the semiconductor manufacturing industry toward intelligent and automated development. This study provides theoretical references for chip manufacturers and establishes a technical foundation for future research.