基于动态激励的三维封装内部缺陷检测

Dynamic excitation based internal defect detection in 3D packaging

硅通孔是一种实现三维封装的关键技术,由于独特的垂直互连方式受到了广泛关注。然而,硅通孔工艺较为复杂,出现缺陷几率增大,而这些缺陷不易被检测,进而影响了封装性能与可靠性。因此,提出一种基于动态激励的内部检测方法,通过对封装芯片施加动态热激励,激发内部缺陷产生异常温度分布,采集封装外表面温度分布时间序列图像,利用深度学习进行识别分类,实现内部缺陷的外部诊断。首先构建三维封装模型进行有限元瞬态热仿真,通过仿真分析揭示了内部缺陷会对温度分布产生细微影响;构建三维卷积神经网络C3D模型,通过分析温度梯度分布图像随时间变化规律来实现对缺陷的识别与分类;搭建试验检测平台,制备含有不同缺陷的三维封装样品进行验证结果表明,基于动态激励的内部缺陷检测方法分类准确率可达到97.81%,可为三维封装内部缺陷的检测提供新思路。

Through Silicon Vias(TSV)is a key technology enabling three dimensional packaging,which has received much attention due to its unique vertical interconnect.However,the complexity of the silicon via process increases the chances of defects,which are not easy to be detected,thereby affecting the performance and reliability of the packaging.Consequently,a dynamic excitation based internal detection method is proposed in this paper.By applying dynamic thermal excitation to the packaged chip,internal defects are stimulated to produce abnormal temperature distributions,and time series images of the temperature distribution on the outer surface of the package are collected,which are recognized and classified using deep learning to achieve external diagnosis of the internal defects.Firstly,a three dimensional packaging model is constructed for transient thermal finite element simulation,and the simulation analysis reveals that internal defects have subtle impacts on the temperature distribution.Then,a three dimensional Convolutional Neural Network(C3D)model is constructed to recognize and classify defects by analyzing the temporal changes in temperature gradient distribution images.Finally,an experimental testing platform is established,and three dimensional packaging samples containing various defects are prepared for validation.The results show that the classification accuracy of the dynamic excitation based internal defect detection method can reach up to 97.81%,offering a new perspective for the detection of internal defects in three dimensional packaging.