PCBA板载DDR芯片焊点缺陷检测研究

Research on solder bump defect detection of DDR chip on PCBA

板载芯片朝着小尺寸高密度方向发展,隐藏于芯片封装内部的微焊球缺陷检测愈发困难。针对工业高密度集成印刷电路板组件(PCBA)板载集成电路(IC)内部故障定位难、效率低的问题,提出一种芯片功能测试过程中采用红外热成像检测结合深度学习的多类型缺陷识别方法。以现场可编程门阵列(FPGA)单板双倍数据速率(DDR)存储芯片为对象,建立了芯片缺陷检测模型,搭建检测平台开展芯片内部焊点故障检测试验研究。设计程序实现芯片的数据存储与读出,同步采集红外图像序列,分析存储芯片读写过程中温度变化,并提取不同敏感测量区域热信号。构建卷积神经网络(CNN)分类模型,并进行超参数调优,实现了内部隐藏缺陷包括不同地址、数据、地址空间焊点故障的高效准确识别。引入迁移学习拓展应用于芯片其他9种不同焊点缺陷的检测,在10、20 dB高斯白噪声条件下分别达到95%、92%以上的准确率,从而为实际工业高密度集成PCBA板载微电子封装及可靠性分析提供了一种快速、有效的方法。

The chip on PCBA is developing towards small size and high density,which make it much difficult to detect micro solder bump defects inside the package.To address the problems of difficulty and low efficiency in locating internal faults of ICs on industrial high-density integrated PCBA,a chip-on-board defect detection method combining the infrared thermal imaging and the deep learning algorithm is proposed,which realizes intelligent defect detection of ICs on PCBA suitable for industrial production scenarios.Taking the real DDR memory chip on FPGA as the target,the infrared defect detection model is formulated,and the test bench is established to conduct experimental research on the fault detection of solder bumps in the chip.The designed program realizes the chip data storage and readout.The infrared image sequence is collected to analyze the temperature evolution of different defect types in the process of DDR chip reading and writing.The thermal signals of different measurement areas are extracted for defects that are difficult to intuitively distinguish by infrared images.With the hyperparameter optimization,the CNN classification model realizes efficient and accurate detection of different defect types,including address,data,and bank address solder joint fault.Furthermore,after transfer learning,the other 9 different solder joint defects of the chip are accurately identified,and the accuracy is over 95%and over 92%under the conditions of 10 and 20 dB Gaussian white noise,respectively.It provides an efficient and effective method for microelectronics packaging and reliability analysis on industrial high-density integrated PCBA.