Model for prediction of package-on-package warpage and the effect of process and material parameters

Abstract

Package-on-Package (PoP) assemblies may experience warpage during package fabrication and later during surface mount assembly. Excessive warpage may result in loss-of-coplanarity, open connections, mis-shaped joints, and reduction in package board-level reliability (BLR) under environmental stresses of thermal cycling, shock and vibration. Previous researchers have shown that warpage may be influenced by a number of design and process factors including underfill properties, mold properties, package geometry, package architecture, board configuration, underfill and mold dispense and cure parameters, package location in the molding panel. A comprehensive inverse model incorporating a full set of design and process parameters and their effect on PoP package and PoP assembly warpage is presently beyond the state of art. In this paper, data has been gathered on multiple package-on-package assemblies under a variety of assembly parameters. The packages have been speckle coated. The warpage of the PoP assemblies have been measured using a glass-top reflow oven using multiple cameras. Warpage measurements have been taken at various temperature of the reflow profile between room temperature and the peak reflow temperature. Finite element models have been created and the package-on-package warpage predictions have been correlated with the experimental data. The experimental data-set has been augmented with the simulation data to evaluate configurations and parameter-variations which were not available in the experimental dataset. Statistical models have been developed to capture the effect of single and multiple parameter variations using principal components regression, and ridge regression. Best subset variables obtained from stepwise methods, have been used for model development. The developed models have been validated with experimental data using a single factor design of experiment study and are found to accurately capture material and geometry effects on part warpage. The results show that the proposed approach has the potential of predicting both single and coupled factor effects on warpage.