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纸质出版日期： 2025-01-16 ，
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胡志强， 吴一全. 2025. 基于机器视觉的半导体晶圆缺陷检测方法综述. 中国图象图形学报， 30(01):0025-0050
Hu Zhiqiang， Wu Yiquan. 2025. Survey of semiconductor wafer defect detection method based on machine vision. Journal of Image and Graphics， 30(01):0025-0050
当今许多新兴科技领域的发展需要集成电路（integrated circuit， IC）技术的支撑。半导体晶圆作为集成电路芯片中的关键角色，因制作工艺复杂，容易产生各种缺陷，其故障会极大地影响芯片的最终工作性能并增加成本。因此半导体晶圆的缺陷检测是保证其良品率和生产率的重要手段。结合机器视觉算法的晶圆缺陷检测方法普适性强、速度快，能更好地满足工业检测的相关需求。鉴于此，综述了近十几年来半导体晶圆缺陷检测方法的研究进展，介绍了晶圆制造的复杂工艺、表面缺陷检测的流程，根据不同的标准将晶圆缺陷进行分类。重点阐述了基于学习的方法，包括有监督机器学习、无监督机器学习、混合学习、半监督学习以及迁移学习5大类。对晶圆缺陷检测的深度神经网络分类为检测网络、分类网络、分割网络与组合网络。随后梳理了在晶圆缺陷检测领域常用的数据集以及性能评价指标。最后总结了晶圆缺陷检测当前存在的问题，对未来的研究方向进行了展望。
The integrated circuit chip is developed on a semiconductor wafer substrate and serves as a fundamental electronic component in many electronic devices. The semiconductor wafer， which is the foundation for manufacturing integrated circuits， contains billions of tiny electronic components. Production is gradually moving toward high-performance and small-sized directions because of the semiconductor industry’s demand for high-quality wafers. In the manufacturing process of wafers， unexpected structures may remain due to environmental， operational， and process-related reasons， leading to a decrease in the operational performance of the wafer chip. These unforeseen structures are referred to as wafer defects. The causes of wafer defects are diverse， with common types including scratches， pits， chemical stains， and dust contamination. The occurrence of unpredictable， mixed， and complex defects can result in increased production costs， decreased product yield， and a decline in manufacturing process stability. Therefore， defect detection on semiconductor wafers is crucial to ensure their performance and improve the yield of high-quality products. Currently， methods for detecting surface defects on semiconductor wafers include manual visual inspection， optical inspection， ultrasonic testing， and machine vision detection. Manual visual inspection involves operators using microscopes or the naked eye to check for obvious defects on the wafer surface， but this method has low efficiency and accuracy for detecting small defects. Optical inspection uses light sources to illuminate the wafer surface and determines the presence of defects by detecting the intensity and shape of reflected and transmitted light. This non-contact method is suitable for large-area defect detection but is less effective for detecting small and shallow defects and cannot identify internal defects within the wafer. Ultrasonic testing utilizes the propagation speed of ultrasonic waves in the wafer， along with the reflection and scattering characteristics of defects such as cracks and voids， but it is sensitive to material density and sound speed， leading to reduced detection effectiveness in specific material. In addition to these three methods， machine vision detection uses image processing and computer vision technology to detect defects on the wafer surface. It is a non-contact， high-speed， automated， cost-effective， and scalable approach， making machine vision-based detection the primary method in the wafer defect detection field today. With the development of computer vision， machine learning， and especially deep learning， the performance of defect identification and classification algorithms for semiconductor wafers has been further enhanced. This not only improves detection efficiency but also reduces production costs and enhances product quality. Machine vision-based detection methods generally fall into three categories： traditional image processing methods， machine learning methods， and deep learning methods. The development of many emerging technology fields today requires the support of integrated circuit technology. As a key role in integrated circuit chips， semiconductor wafers are prone to various defects due to complex manufacturing processes， and their failures will greatly affect the final performance of the chip and increase costs. Therefore， defect detection of semiconductor wafers is an important means to ensure its yield and productivity. Wafer defect detection combined with machine vision algorithms has strong universality and high speed and can better meet the relevant needs of industrial inspection. To have a deeper understanding of the research status of wafer defect detection， this paper conducts a comprehensive and thorough research on the defect detection method based on machine vision. Wafer manufacturing， its surface defect detection process， and the classification of wafer defects are introduced. The detection methods based on traditional image processing， machine learning， and deep learning algorithms are fully explored. Detection methods based on traditional image processing are divided according to the spatial domain and the transform domain. Detection methods based on the spatial domain include template matching， edge gradient， optical flow method， and spatial filtering method and are divided according to the correlation between the defect pattern and the template or the dissimilarity of the background. Detection methods based on the transform domain include wavelet transform， frequency filtering， and Fourier transform. Further research is being conducted on methods based on Fourier transform in the wavelet transform domain. It focuses on the application of machine learning and deep neural network in wafer defect detection. According to the learning method， the learning techniques utilized for wafer defect identification are divided into supervised machine learning， unsupervised machine learning， hybrid learning， semi-supervised learning， and transfer learning； according to the network category， deep neural networks for wafer defect detection is divided into detection network， classification network， segmentation network， and combination network. The detection performance and advantages and disadvantages of each method are compared in depth in each section. From the collected literature， it indicates that research on wafer defect detection based on transformer is increasing year by year. The large datasets shared in this field mainly include WM-811K and MixedWM38. Most of the datasets are not open to the public and are maintained and updated by teams or companies. Representative evaluation indicators mainly include single-label indicators of supervised learning/semi-supervised learning （accuracy， precision， recall， F1 score） and multi-label indicators （micro-precision， micro-recall， exact match ratio， Hamming loss）. Unsupervised learning indicators include Rand index， adjusted Rand index， normalized mutual information， adjusted mutual information， and purity. Finally， the existing problems of the current vision-based wafer defect detection are identified， namely， low data availability， serious class imbalance problem， high computational complexity， and limited research on mixed-type defects. Future development trends include multimodal and non-destructive detection techniques and more efficient feature representation learning.
半导体晶圆缺陷检测机器视觉深度神经网络（DNN）机器学习晶圆缺陷数据集
semiconductor waferdefect inspectionmachine visiondeep neural network（DNN）machine learningwafer defect dataset
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