Research on physical feature analysis of integrated circuits based on machine learning

An Yuan; Zhaoquan He; Yanlin Chen

doi:10.1117/12.2684648

19 October 2023 Research on physical feature analysis of integrated circuits based on machine learning

An Yuan, Zhaoquan He, Yanlin Chen

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270925 (2023) https://doi.org/10.1117/12.2684648
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Based on the application requirements of physical feature recognition of integrated circuits, this study will use YOLOv5, are presentative of the YOLO series algorithms, to improve and optimize them from the theoretical and experimental perspectives, in order to obtain a faster and more accurate detection model in precision-type target detection tasks. This experiment will detail the basic architecture of the YOLOv5 model, and make up for the shortcomings of the YOLOv5 series model in terms of detection accuracy and break through the model bottleneck by appropriately replacing core components, introducing spatial attention mechanism, channel attention mechanism, and improving the loss function and nonlinear function.

Citation Download Citation

An Yuan, Zhaoquan He, and Yanlin Chen "Research on physical feature analysis of integrated circuits based on machine learning", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270925 (19 October 2023); https://doi.org/10.1117/12.2684648

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