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Reliability Assessment of Wafer Level Package using Artificial Neural Network Regression Model

Published online by Cambridge University Press:  14 November 2019

P. H. Chou ,
K.N. Chiang Open the ORCID record for K.N. Chiang [Opens in a new window]  and
Steven Y. Liang
P. H. Chou
Affiliation:
Advanced Microsystem Packaging and Nano-Mechanics Research Lab, Taiwan Dept. of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan, R.O.C.
K.N. Chiang*
Affiliation:
Advanced Microsystem Packaging and Nano-Mechanics Research Lab, Taiwan Dept. of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan, R.O.C.
Steven Y. Liang
Affiliation:
Georgia Institute of Technology, George W Woodruff School of Mechanical Engineering, Atlanta, USA
*
*Corresponding author (knchiang@pme.nthu.edu.tw *)
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Abstract

For electronic packaging structure, there are many design parameters that will affect its reliability performance, using experimental way to obtain the reliability result will take a considerable amount of time. Therefore, how to shorten the design time becomes a critical issue for new electronic packaging structure development. This research will combine artificial intelligence (AI) and simulation technology to assess the long-term reliability of wafer level packaging (WLP). A simulation technology using finite element method (FEM) with appropriate mechanics theories has been validated by multiple experiments will replace the experiment to create reliability results for different WLP structures. After a big WLP structure-reliability database created, this study will apply artificial neural network (ANN) theory to analyze this database and obtains a regression model for structure-reliability relationship of WLP. Once the regression model is established and validated, the WLP geometry, such as pad size, die and buffer layer thickness, and solder volume, etc. can be simply entered, and then the WLP reliability results can be immediately obtained through the ANN regression model.

Keywords

FEM simulationWSPMachine learningArtificial neural networkRegression model
Type
Research Article
Information
Journal of Mechanics , Volume 35 , Issue 6 , December 2019 , pp. 829 - 837
Copyright
© The Society of Theoretical and Applied Mechanics 2019 

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References

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