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基于机器视觉的芯片偏移检测系统 被引量:7

Die position offset detection system based on machine vision
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摘要 针对矩阵式测试盘上芯片偏移检测问题,搭建一套基于机器视觉的芯片偏移检测系统,主要包括上下料流道、三维移动平台和视觉检测模块。检测系统以一标准样板为基准,构建模板匹配与仿射变换相结合的图像矫正算法,实现不同待测样本间检测的通用性;以灰度梯度及梯度方向为依据,设计针对芯片区域的矩形测量算法;系统以芯片左上角和右下角点连线的中心为基准点计算芯片偏移量。实验结果表明,本系统针对芯片偏移检测的误差范围-2.145~4.257μm,单片芯片偏移计算算法执行的平均时间为72.56 ms,检测轴运行速度为20 mm/s,对行列数为5×12的矩阵式测试盘上芯片的平均检测时间为64.5 s/盘,可满足实际加工过程的需求;使用测量系统分析(MSA)中的偏倚与线性度分析方法对本系统的准确性进行评估,结果表明系统的偏倚和线性均满足生产需求。 For the problem of offset detection of dies on matrix test trays,a vision-based die offset detection system is built,which mainly includes loading and off-loading channels,a 3D moving platform,and a vision detection module.This system takes a standard sample as the benchmark and builds an image correction algorithm combining template matching and affine transformation to achieve the generality between different inspections.A rectangular measurement algorithm for a specific area is designed,based on the grey scale gradient and the direction of the gradient;the system calculates the die offset using center coordinates of the line which connecting the top left and bottom right points of the die as the reference point.The experimental results show as follows:The offset detection error range of this system is-2.145 to 4.257μm,the average time of algorithm execution is 72.56 ms,and the operation speed is 20 mm/s,and the average detection time for chips on matrix test trays with 5×12 is 64.5 s/tray,which can meet the demand of the actual processing process;the accuracy of this system is tested using the bias and linearity analysis method in measurement system analysis(MSA),the linearity of bias is significant.To conclude,the bias and linearity of the system meet the requirements.
作者 周钰 孙力 陈思蓉 孙箐源 夏锐 Zhou Yu;Sun Li;Chen Sirong;Sun Qingyuan;Xia Rui(School of Mechanical Engineering,Jiangsu University,Zhenjiang 212000,China)
出处 《电子测量与仪器学报》 CSCD 北大核心 2022年第12期229-236,共8页 Journal of Electronic Measurement and Instrumentation
关键词 芯片 偏移检测 机器视觉 边缘检测 测量系统分析 die offset detection machine vision edge detection MSA
作者简介 通信作者:周钰,2020年于江苏大学获得学士学位,现为江苏大学硕士研究生,主要研究方向为数字图像处理与应用。E⁃mail:1275954172@qq.com
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