The Optimization of Wire Bonding Parameters and Quality Prediction Model Based on Neural Network

Abstract:

In this article, the parameters affecting the quality of wire bonding are analyzed by orthogonal testing with the methods of variance analysis and F tests. By analyzing the results, parameters that have a major impact on the quality of wire bonding are optimized. Because the relationship is complicated and non-linear between the impacting parameters and bonding quality, this article introduces a neural network algorithm of BPNN to build a model describing it. The structural parameters of the neural network are identified and a quality prediction model of wire bonding is established in this article. The model is validated, the results show that this proposed model has higher precision and it can accurately reflect the trends of the bonding quality indicators.

• Periodical: Advanced Materials Research (Volumes 542-543)
• Pages: 976-980
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.542-543.976
• Online since: June 2012
• Authors: Xiao Dan Guan, Gang Chen, Wan Lei Liang
• Keywords: Neural Network (NN), Orthogonal Testing, Quality Prediction, Wire Bonding

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