基于耦合信号包络分析的引线键合点剪切强度识别方法研究

键合点剪切强度是衡量键合质量的重要指标之一，以引线键合系统超声波发生器电信号作为信息载体，研究了超声电信号的特征提取方法以及在键合强度识别方面的应用。针对超声电信号的瞬态特性，提出一种基于信号子频带包络分段的特征提取方法。该方法将超声电信号滤波后的子频带包络分成信号上升、稳定和衰减三个阶段，提取每个阶段的敏感波形特征来表征键合过程。为了消除特征中的冗余信息并实现特征降维，采用了主分量分析(PCA)技术进行特征选择。建立了人工神经网络系统(ANN)对提取的特征进行识别,通过实验数据分析，验证了本文方法的有效性。

Identifying wire bond shear strength via analysis of envelope of coupled signal

The wire bond shear strength is one of important index of bond quality. The paper using the electrical signal from the ultrasonic generator supply as the information carrier, the feature extraction method of ultrasonic electrical signals in bond shear strength identification is investigated. A new feature extraction method based on the subband envelope segmentation is proposed for characterizing the transient ultrasonic electrical signal. The filtered subband envelopes of ultrasonic electrical signal is separated into three phases individually, namely envelope rising phase, stable and damping phase. The waveform features are extracted from each phase of the envelope for further bond shear strength identification. To remove the irrelevant information and reduce the dimension of original feature variables, the principal component analysis is carried out for the feature selection. Using the selected features as inputs, an artificial neural network (ANN) is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in identifying bond shear strength.