Affiliation:
1. School of Marine Engineer Equipment, Zhejiang Ocean University, Zhoushan, China
2. Dong hai Institute of Science and Technology, Zhejiang Ocean University, Zhoushan, China
Abstract
Imperfection in a bonding point can affect the quality of an entire integrated circuit. Therefore, a time–frequency analysis method was proposed to detect and identify fault bonds. First, the bonding voltage and current signals were acquired from the ultrasonic generator. Second, with Wigner–Ville distribution and empirical mode decomposition methods, the features of bonding electrical signals were extracted. Then, the principal component analysis method was further used for feature selection. Finally, an artificial neural network was built to recognize and detect the quality of ultrasonic wire bonding. The results showed that the average recognition accuracy of Wigner–Ville distribution and empirical mode decomposition was 78% and 93%, respectively. The recognition accuracy of empirical mode decomposition is obviously higher than that of the Wigner–Ville distribution method. In general, using the time–frequency analysis method to classify and identify the fault bonds improved the quality of the wire-bonding products.
Funder
zhejiang ocean university
young and middle-aged discipline leaders of Zhejiang Province
bureau of science and technology of zhoushan
Subject
Computer Networks and Communications,General Engineering
Cited by
4 articles.
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