Zhang Guang-Ming, Harvey David M, Braden Derek R
General Engineering Research Institute, Liverpool John Moores University, Liverpool, United Kingdom.
Ultrasonics. 2006 Dec;45(1-4):82-91. doi: 10.1016/j.ultras.2006.07.005. Epub 2006 Jul 31.
Recently, adaptive sparse representations of ultrasonic signals have been utilized to improve the performance of scanning acoustic microscopy (SAM), a common nondestructive tool for failure analysis of microelectronic packages. The adaptive sparse representation of an ultrasonic signal is generated by decomposing it in a learned overcomplete dictionary using a sparse basis selection algorithm. Detection and location of ultrasonic echoes are then performed on the basis of the resulting redundant representation. This paper investigates the effect of sparse basis selection algorithms on ultrasonic signal representation. The overcomplete independent component analysis, focal underdetermined system solver (FOCUSS), and sparse Bayesian learning algorithms are examined. Numerical simulations are performed to quantitatively analyze the efficiency of ultrasonic signal representations. Experiments with ultrasonic A-scans acquired from flip-chip packages are also carried out in the study. The efficiency of ultrasonic signal representations are evaluated in terms of the different criteria that can be used to measure its performance for different SAM applications, such as waveform estimation, echo detection, echo location and C-scan imaging. The results show that the FOCUSS algorithm performs best overall.
最近，超声信号的自适应稀疏表示已被用于提高扫描声学显微镜（SAM）的性能，SAM是用于微电子封装失效分析的一种常见无损工具。超声信号的自适应稀疏表示是通过使用稀疏基选择算法在一个学习得到的超完备字典中对其进行分解而生成的。然后基于所得的冗余表示来执行超声回波的检测和定位。本文研究了稀疏基选择算法对超声信号表示的影响。研究了超完备独立分量分析、聚焦欠定系统求解器（FOCUSS）和稀疏贝叶斯学习算法。进行了数值模拟以定量分析超声信号表示的效率。在该研究中还开展了对从倒装芯片封装获取的超声A扫描的实验。根据可用于测量其在不同SAM应用（如波形估计、回波检测、回波定位和C扫描成像）中的性能的不同标准，对超声信号表示的效率进行了评估。结果表明，总体而言FOCUSS算法表现最佳。
