
FALCO-WAFER: Feature-Aware Lightweight Contextual Detector for Wafer Defect Detection
IEEE International Test Conference in Asia (ITC-Asia), pp. 43–47, 2025.
面向半导体晶圆检测的轻量化特征感知模型。在 5,723 张标注缺陷图像上,达到 90.7% AP@0.5、7.19% 漏检率,参数量为 13.3M。
关于这项研究
模型结合用于高效纹理编码的多尺度深度卷积模块与用于特征优化的 Token-Energy Diagonal Attention 检测头,在保持紧凑模型的同时,针对晶圆上细微、低对比度缺陷开展检测研究。我的工作涉及数据准备、模型基准测试、结构改进和论文写作。
引用这篇论文
@inproceedings{zhang2025falcowafer,
title={FALCO-WAFER: Feature-Aware Lightweight Contextual Detector for Wafer Defect Detection},
author={Zhang, Haotian and Cao, Shurong and Zou, Ningmu},
booktitle={2025 IEEE International Test Conference in Asia (ITC-Asia)},
pages={43--47},
year={2025},
doi={10.1109/ITC-Asia67627.2025.00016}
}下载 .bib