Improved Color Defect Detection With Machine Learning for After Develop Inspections in Lithography

McLaughlin, Matthew P.; Mennell, Petra; Stamper, A. K.; Barber, Gabriel; Paduano, Janice; Benn, Emerson; Linnane, Michael; Zwick, Justin; Khatumria, Chetan; Isaacson, Robert L.; Hoffman, Nathan; Menser, Clayton

doi:10.1109/tsm.2022.3186607

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…These two defects are the main cause of chip failure. Unpa erned wafer defects mostly occur in the pre-lithography stage of wafer production [6], which are wafer defects caused by machine failures. The scratch defect is shown in Figure 1a, and the particle contamination defect is shown in Figure 1b.…”

Section: Wafer Surface Defect Pa Ernsmentioning

confidence: 99%

Review of Wafer Surface Defect Detection Methods

et al. 2023

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Wafer surface defect detection plays an important role in controlling product quality in semiconductor manufacturing, which has become a research hotspot in computer vision. However, the induction and summary of wafer defect detection methods in the existing review literature are not thorough enough and lack an objective analysis and evaluation of the advantages and disadvantages of various techniques, which is not conducive to the development of this research field. This paper systematically analyzes the research progress of domestic and foreign scholars in the field of wafer surface defect detection in recent years. Firstly, we introduce the classification of wafer surface defect patterns and their causes. According to the different methods of feature extraction, the current mainstream methods are divided into three categories: the methods based on image signal processing, the methods based on machine learning, and the methods based on deep learning. Moreover, the core ideas of representative algorithms are briefly introduced. Then, the innovations of each method are compared and analyzed, and their limitations are discussed. Finally, we summarize the problems and challenges in the current wafer surface defect detection task, the future research trends in this field, and the new research ideas.

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Section: Wafer Surface Defect Pa Ernsmentioning

confidence: 99%

Review of Wafer Surface Defect Detection Methods

et al. 2023

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AI/ML algorithms and applications in VLSI design and technology

et al. 2023

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An efficient deep learning framework for mixed-type wafer map defect pattern recognition

Sheng,

Cheng,

Jin

et al. 2024

AIP Advances

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Defect detection on wafers holds immense significance in producing micro- and nano-semiconductors. As manufacturing processes grow in complexity, wafer maps may display a mixture of defect types, necessitating the utilization of more intricate deep learning models for effective feature learning. However, sophisticated models come with a demand for substantial computational resources. In this paper, we propose an efficient deep learning framework designed explicitly for mix-type wafer map defect pattern recognition. Our proposed model incorporates several crucial design elements, including lightweight convolutions, bottleneck residual connections, efficient channel attention mechanisms, and optimized activation functions, enabling it to learn spatial and channel features efficiently. We conduct evaluations on a real-world dataset containing 38 mixed-type defect patterns. The experimental results demonstrate that our framework maintains a high level of accuracy while possessing a compact parameter size and remarkably fast inference speed.

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Improved Color Defect Detection With Machine Learning for After Develop Inspections in Lithography

Cited by 5 publications

References 10 publications

Review of Wafer Surface Defect Detection Methods

Review of Wafer Surface Defect Detection Methods

AI/ML algorithms and applications in VLSI design and technology

An efficient deep learning framework for mixed-type wafer map defect pattern recognition

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