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10 December 2024 Integrated circuit defect classification based on multi-layer attention mechanisms
Botong Zhao, Yue Lu, Kan Zhou, Wenzhan Zhou
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Proceedings Volume 13423, Eighth International Workshop on Advanced Patterning Solutions (IWAPS 2024); 1342319 (2024) https://doi.org/10.1117/12.3053071
Event: 8th International Workshop on Advanced Patterning Solutions (IWAPS 2024), 2024, Jiaxing, Zhejiang, China
Abstract
In the integrated circuit (IC) manufacturing process, defects directly impact the final product yield. Integrated circuit defects are characterized by a wide variety of defect types and complex circuit structures. We proposes a defect detection model based on multi-layer attention mechanisms, which enables the detection and classification of common defects in etching processes. First, we use a pre-trained backbone to extract features from different layers. Then, we perform feature encoding and fusion across these different layers. Finally, we utilize an end-to-end decoder to determine the location and type of defects. Compared to similar methods, our method shows a significant improvement in accuracy across different types of defects and requires fewer training samples. Some types of defects have already met the application requirements, and our approach incurs lower training costs when dealing with new types of defects, necessitating only fine-tuning of the model rather than retraining the entire network.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Botong Zhao, Yue Lu, Kan Zhou, and Wenzhan Zhou "Integrated circuit defect classification based on multi-layer attention mechanisms", Proc. SPIE 13423, Eighth International Workshop on Advanced Patterning Solutions (IWAPS 2024), 1342319 (10 December 2024); https://doi.org/10.1117/12.3053071
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KEYWORDS
Object detection
Defect detection
Feature extraction
Integrated circuits
Education and training
Detection and tracking algorithms
Manufacturing
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