 Application research on the time–frequency analysis method in the quality detection of ultrasonic wire bondingWuwei Feng, Xin Chen, Cuizhu Wang and Yuzhou Shi International Journal of Distributed Sensor Networks, 2021, vol. 17, issue 5, 15501477211018346 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. Keywords: Ultrasonic wire bonding; quality detection; time–frequency analysis; feature extraction; feature selection; pattern recognition (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:17:y:2021:i:5:p:15501477211018346 DOI: 10.1177/15501477211018346 Access Statistics for this article More articles in International Journal of Distributed Sensor Networks |
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