A Wafer Segmentation Method Using the Closest Affine Iterative Point

doi:10.7652/xjtuxb201712009

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A Wafer Segmentation Method Using the Closest Affine Iterative Point

更新时间：2025-07-09

- A Wafer Segmentation Method Using the Closest Affine Iterative Point
- Vol. 51, Issue 12, Pages: 56-61(2017)
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,西安,710049
  2. 西安交通大学机械结构强度与振动国家重点实验室,西安,710049
  3. 西安交通大学陕西省先进飞行器服役环境与控制重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201712009
  CLC： TP391.41
- Online First：10 December 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
A Wafer Segmentation Method Using the Closest Affine Iterative Point[J]. 2017, 51(12): 56-61.
DOI：

A Wafer Segmentation Method Using the Closest Affine Iterative Point[J]. 2017, 51(12): 56-61. DOI： 10.7652/xjtuxb201712009.

摘要

针对依赖硬件设施的晶圆分割方法存在生产成本高、工艺复杂且分割效果不稳定的问题

提出以仿射迭代最近点(ICP)算法为核心的基于图像形状配准思想的晶圆分割方法。该方法采用Canny算子提取图像边缘

建立晶圆模板图像与目标图像的特征点集; 对目标图像的边缘图像进行基于Hough的直线检测

得到粗略的晶圆矩形边框信息; 以矩形左上角的点坐标作为匹配搜索区域的初始值进行基于仿射ICP算法的精确配准

通过晶圆产品图像与模板图像的特征匹配

实现晶圆的快速、准确分割。理论分析及实验结果表明:该方法计算复杂度较低

单独样本分割时间约为0.9 s

样本分割精度明显高于其他算法

满足自动化生产线的实时在线检测需求。

Abstract

A novel wafer segmentation method based on the closest affine iterative point(ICP)is proposed to address the problems inherent in wafer segmentation relying on the hardware instruments

such as high production cost

complicated process and unstable segmentation effects. The method is independent on hardware facilities

and establishes the feature point sets of the wafer template image and the target image by utilizing the Canny operator to extract edges of the images and obtains the rough wafer rectangle information through Hough's straight line detection. The affine ICP algorithm is then used to perform precise registration with the initial value that is the coordinates of the upper left corner of the rectangle. The model image is matched with the reference image which results in a rapid and accurate wafer segmentation. Theoretical analysis and experimental results show that the computational complexity of the method is low and a single sample segmentation takes no more than 0.9 seconds

while the sample segmentation accuracy is significantly higher than those of other algorithms. Therefore

the proposed method satisfies the real-time detection requirements of automatic production lines.

关键词

Keywords

references

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