Sparse Denoising Method for High-frequency Ultrasonic Detection Signals of Flip Chip Based on Training Local Dictionary

doi:10.3901/JME.2023.06.010

Abstract

Abstract: To reduce the influence of noise on the high-frequency ultrasonic detecting accuracy of flip chip defects and overcome the problem of high dimension of high-frequency ultrasonic signal, a sparse de-noising method of high-frequency ultrasonic signal based on K-Singular value decomposition(K-SVD) training local dictionary is proposed. In this study, K-SVD is used to train the dictionary to reduce the error between the signal and the atoms of the dictionary. Aiming at the problem that K-SVD can’t train high dimensional dictionary, the high-frequency ultrasound signal is cut into multi segment local signals with lower dimension which are decomposed sparsely in low dimensional dictionary to reduce the computational complexity of training dictionary and sparse decomposition. Then, the global maximum posteriori probability(MAP) estimation is used to reconstruct the signal to eliminate the signal jump caused by local processing and realize the de-noising of high-frequency ultrasonic signal. The results of simulation and experiments show that the proposed method can effectively remove the noise in high-frequency ultrasonic signal. Compared with sparse decomposition of high-frequency ultrasonic signal in global dictionary, the sparse decomposition in local training dictionary can ensure the de-noising performance and reduce the computational complexity.

Key words: high-frequency ultrasonic detection, K-SVD local dictionary, maximum a posteriori probability, flip chip

CLC Number:

TH89

SU Lei, TAN Shihong, JI Yong, MING Xuefei, GU Jiefei, LI Ke. Sparse Denoising Method for High-frequency Ultrasonic Detection Signals of Flip Chip Based on Training Local Dictionary[J]. Journal of Mechanical Engineering, 2023, 59(6): 10-17.

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