SMTA International Conference Proceedings


Author: Satyanarayan Iyer et al.
Company: SMART Modular Technologies
Date Published: 9/25/2005   Conference: SMTA International

Seika Machinery, Inc.

Abstract: Selecting an appropriate underfill and a compatible flux is crucial to establish a reliable flip chip assembly. Underfill, being an epoxy material, tends to absorb moisture. The moisture absorbed by underfill has a detrimental effect on reliability of flip chip packages. Researchers in the past have conducted experiments to qualify different underfillflux combinations based on their moisture sensitivity. The results of these experiments were however limited to qualifying a particular underfill-flux combinations that were tested.

The current research focuses on developing models that relate the assembly reliability with the properties of underfills and fluxes. The important properties of underfills and flux and their significance on the failures were studied using statistical techniques. Factors that were found to be significant were used for subsequent modeling. These models were developed using experimental data from JEDEC Level 3 testing of flip chip packages that used different underfill-flux combinations. Results for 95 different underfill-flux combinations were available for modeling. All other parameters except for the underfill-flux combinations were the same across all the data.

Regression and backpropagation neural network models were developed and validated using two approaches. The first approach was the commonly used approach of dividing the available data points into sets for model building and testing (validation) and second was the cross-validation approach. The results indicated that the neural network models performed better than the corresponding regression models in relating the failures with the selected properties of the underfill and flux. The neural network approach of modeling was recommended based on the results obtained as a part of this research effort and also due to the ability of neural networks to better capture non-linearaties.

Keywords: Flip chip, underfill, flux, regression, neural networks.

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