Lei Su1, Shihong Tan2, Yang Qi2, Jiefei Gu1, Yong Ji2, Gang Wang2, Xuefei Ming2, Ke Li1 and Michael Pecht3
1Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China
2The 58th Research Institute of China Electronics Technology Group Corporation, Wuxi 214000, China
3Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
For more information about this article and related research, please contact Prof. Michael Pecht
Noise suppression of an echo signal plays an important role in high-frequency ultrasonic testing of flip chips. This paper proposes an orthogonal matching pursuit (OMP) method optimized by an improved artificial bee colony (ABC) algorithm for denoising high-frequency ultrasonic testing signals of flip chips. We add adaptive learning factors to change the way the ABC randomly selects the search direction, which speeds up the convergence speed of the algorithm. The improved ABC, named adaptive artificial bee colony (AABC), replaces the greedy search process of OMP. Instead of searching for the atom that best matches the echo signal, the improved OMP algorithm searches for the optimal parameter and replaces the atom with a set of parameters with practical physical significance. The introduction of the AABC changes the search space of OMP from discrete dictionary space to continuous parameter space, which minimizes the error between the atom and echo signal and leads to an accurate approximation. Additionally, the AABC reduces the search times of OMP and improves the convergence speed. Then, the wavelet transform thresholding technology is combined with the attenuation characteristic of high-frequency ultrasound to eliminate the influence of error decomposition caused by noise and inaccurate sparsity setting. We present the noise suppression experimental results of high-frequency ultrasonic simulation and real testing signals of flip chips, including Gaussian white noise and correlated noise, and compare the proposed method with other sparse representation methods, including matching pursuit (MP) and OMP. The results demonstrate the superior performance of the proposed method.
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