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厦门大学 萨本栋微米纳米科学技术研究院,福建 厦门 361000
[ "王若宇(1988-),男,福建漳州人,博士研究生,2011年于上海交通大学获得学士学位,2017年于厦门大学获得硕士学位,主要从事半导体AOI设备的算法研究。E-mail:2322020015603 6@stu.xmu.edu.cn" ]
纸质出版日期:2024-10-10,
收稿日期:2024-06-19,
修回日期:2024-07-24,
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王若宇,严富洋,刘暾东.晶圆飞拍成像系统的快速均匀曝光控制[J].光学精密工程,2024,32(19):2933-2944.
WANG Ruoyu,YAN Fuyang,LIU Tundong.Rapid and uniform exposure control for wafer motion imaging system[J].Optics and Precision Engineering,2024,32(19):2933-2944.
王若宇,严富洋,刘暾东.晶圆飞拍成像系统的快速均匀曝光控制[J].光学精密工程,2024,32(19):2933-2944. DOI: 10.37188/OPE.20243219.2933.
WANG Ruoyu,YAN Fuyang,LIU Tundong.Rapid and uniform exposure control for wafer motion imaging system[J].Optics and Precision Engineering,2024,32(19):2933-2944. DOI: 10.37188/OPE.20243219.2933.
晶圆飞拍成像系统的曝光控制对成像质量具有显著影响,传统基于空间域评价的曝光控制算法计算复杂且忽视了晶粒特征的差异,易导致曝光不均匀。为此,提出一种图像分区频率域评价的曝光控制方法。该方法采用分块的双阈值分割算法,对晶粒图像的特征区域进行自适应分割;结合高通滤波器和高斯金字塔算法,在频率域内快速提取图像高频信息,并设计一种区域加权评价函数对曝光效果进行综合评价。为实现在最大曝光时间内对最优曝光参数的快速搜索,提出了基于决策树的变步长搜索算法。对比实验结果表明:该搜索算法在保证准确率的前提下,使最佳图像平均质量指标提高1.34%,曝光参数调整时间减少61.3%,有效地保证了晶圆飞拍成像过程中的成像质量。
The exposure control of wafer motion imaging systems greatly affects image quality. Traditional spatial domain-based algorithms are complex and overlook differences in die features, causing uneven exposure. To solve this, we propose a method using frequency domain evaluation through image partitioning. This method uses a block-based dual-threshold segmentation algorithm to adaptively segment image features. By combining high-pass filtering with a Gaussian pyramid algorithm, it efficiently extracts high-frequency information. A region-weighted evaluation function is crafted to evaluate uniform exposure effects. Furthermore, a decision tree-based search algorithm with variable steps is introduced to quickly find optimal exposure parameters. Experiments show this algorithm improves image quality by 1.34% and reduces exposure adjustment time by 61.3%, ensuring fast imaging quality in wafer motion imaging.
曝光控制频率域分析图像评价函数变步长搜索决策树
exposure controlfrequency domain analysisimage evaluation functionvariable step searchdecision tree
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