基于主动红外激励的硅通孔内部多缺陷分类与定位  

Multi-Defect Classification and Localization of Through-Silicon Vias Based on Active Infrared Excitation

作　　者：聂磊[1] 刘江林 张鸣 骆仁星 Nie Lei;Liu Jianglin;Zhang Ming;Luo Renxing(School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,Hubei,China;Hubei Taihe Electric Co.,Ltd.,Xiangyang 441057,Hubei,China)

机构地区：[1]湖北工业大学机械工程学院,湖北武汉430068 [2]湖北泰和电气有限公司,湖北襄阳441057

出　　处：《激光与光电子学进展》2024年第12期134-142,共9页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(51975191)。

摘　　要：随着硅通孔(TSV)封装密度的不断提高,检测隐藏于封装内部的缺陷愈发困难。针对TSV内部多缺陷检测难度大、效率低的问题,提出一种基于主动红外激励的TSV内部多缺陷分类与定位方法。首先,通过仿真分析掌握TSV内部缺陷在主动激励下的外部表现规律。然后,构建卷积神经网络分类模型对仿真数据进行训练,以实现内部多缺陷的分类识别与定位。最后,搭建封装内部缺陷检测平台开展内部缺陷检测实验研究,以近红外激光为主动激励激发封装模型的内部缺陷,接着通过红外热像仪采集图像并输入卷积神经网络进行分类。结果表明,该方法能在不损坏样品的前提下有效识别缺陷的类型及位置,准确率可达96.20%,为TSV三维封装的可靠性分析提供了一种可靠的途径。With the continuous increase in through-silicon via(TSV)packaging density,the detection of the defects hidden inside the package has become increasingly challenging.To address the difficulties in detecting multiple defects inside TSVs and the low efficiency of such detection,a method based on active infrared excitation for TSV internal defect classification and localization is proposed.First,through simulation analysis,the external performance patterns of TSV internal defects under active excitation are studied.Then,a convolutional neural network classification model is constructed and trained using simulated data to achieve the classification and localization of multiple internal defects.Finally,a platform for detecting internal defects in packaging is established to conduct experimental research.Nearinfrared laser is used as the active excitation to stimulate internal defects in the packaging model.Infrared thermography is employed to capture images,which are then fed into the convolutional neural network for classification.The results show that this method can effectively identify the defect types and locations without damaging the samples,with an accuracy rate reaching 96.20%.It provides a reliable approach for the reliability analysis of TSV 3D packaging.

关 键 词：无损检测 硅通孔 内部缺陷 主动红外 卷积神经网络 

分 类 号：TN305.94[电子电信—物理电子学]