Imperfection in a bonding point can affect the quality of an entire integrated circuit. Therefore, a time–frequency analysis method was proposed to detect and identify fault bonds. First, the bonding voltage and current signals were acquired from the ultrasonic generator. Second, with Wigner–Ville distribution and empirical mode decomposition methods, the features of bonding electrical signals were extracted. Then, the principal component analysis method was further used for feature selection. Finally, an artificial neural network was built to recognize and detect the quality of ultrasonic wire bonding. The results showed that the average recognition accuracy of Wigner–Ville distribution and empirical mode decomposition was 78% and 93%, respectively. The recognition accuracy of empirical mode decomposition is obviously higher than that of the Wigner–Ville distribution method. In general, using the time–frequency analysis method to classify and identify the fault bonds improved the quality of the wire-bonding products.

结合点的缺陷会影响整个集成电路的质量。因此，提出了一种时频分析方法来检测和识别断层键。首先，从超声发生器获取键合电压和电流信号。其次，利用Wigner-Ville分布和经验模式分解方法，提取了电信号键合的特征。然后，将主成分分析方法进一步用于特征选择。最后，建立了一个人工神经网络来识别和检测超声波引线键合的质量。结果表明，Wigner-Ville分布和经验模态分解的平均识别准确率分别为78％和93％。经验模态分解的识别精度明显高于Wigner-Ville分布方法。通常，使用时频分析方法对故障键进行分类和识别可以提高引线键合产品的质量。