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  

Identifying wire bond shear strength based on envelope analysis of coupled signals

作　　者：冯武卫[1] 孟庆丰[2] 

机构地区：[1]浙江海洋学院机电工程学院,舟山316000 [2]西安交通大学机械学院轴承研究所,西安710049

出　　处：《振动与冲击》2011年第10期153-159,共7页Journal of Vibration and Shock

基　　金：国家自然科学基金项目资助(50475087;50875196)

摘　　要：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,验证了该方法的有效性。The wire bond shear strength is one of important indexes of bond quality.Using electrical signals produced by an ultrasonic generator as an information carrier,a feature extraction method of ultrasonic electrical signals in bond shear strength identification was investigated.A new feature extraction method based on a subband envelope segmentation was proposed for characterizing transient ultrasonic electrical signals.The filtered subband envelopes of ultrasonic electrical signals were separated into three phases individually,namely,envelope rising phase,stable one and decay one.The waveform features were extracted from each phase of one envelope for further bond shear strength identification.To remove the irrelevant information and reduce the dimension number of original feature variables,the principal component analysis was performed for feature selection.Using the selected features as inputs,an artificial neural network（ANN） was constructed to identify the complex bond fault pattern.By analyzing experimental data with the proposed feature extraction method,the results demonstrated the effective-ness of the proposed feature extraction method and the constructed artificial neural network in identifying bond shear strength.

关 键 词：引线键合 超声电信号 特征提取 主分量分析 键合点剪切强度 

分 类 号：TN605[电子电信—电路与系统]