Paper Info
Title
Board level solder joint reliability modeling and testing of TFBGA packages for telecommunication applications
Abstract
For thin-profile fine-pitch BGA (TFBGA) packages, board level solder joint reliability during the thermal cycling test is a critical issue. In this paper, both global and local parametric 3D FEA fatigue models are established for TFBGA on board with considerations of detailed pad design, realistic shape of solder joint, and nonlinear material properties. They have the capability to predict the fatigue life of solder joint during the thermal cycling test within ±13% error. The fatigue model applied is based on a modified Darveaux’s approach with nonlinear viscoplastic analysis of solder joints. A solder joint damage model is used to establish a connection between the strain energy density (SED) per cycle obtained from the FEA model and the actual characteristic life during the thermal cycling test. For the test vehicles studied, the maximum SED is observed at the top corner of outermost diagonal solder ball. The modeling predicted fatigue life is first correlated to the thermal cycling test results using modified correlation constants, curve-fitted from in-house BGA thermal cycling test data. Subsequently, design analysis is performed to study the effects of 14 key package dimensions, material properties, and thermal cycling test condition. In general, smaller die size, higher solder ball standoff, smaller maximum solder ball diameter, bigger solder mask opening, thinner board, higher mold compound CTE, smaller thermal cycling temperature range, and depopulated array type of ball layout pattern contribute to longer fatigue life.
Year
DOI
Venue
2003
10.1016/S0026-2714(03)00127-6
Microelectronics Reliability
Keywords
Field
DocType
curve fitting,material properties,strain energy density,thermal cycling
Ball grid array,Solder mask,Viscoplasticity,Temperature cycling,Soldering,Test data,Strain energy density function,Engineering,Material properties,Structural engineering
Journal
Volume
Issue
ISSN
43
7
0026-2714
Citations 
PageRank 
References 
12
9.31
0
Authors
5
Name
Order
Citations
PageRank
Tong Yan Tee18427.45
Hun Shen Ng25621.96
Daniel Yap32112.38
Xavier Baraton41610.91
Zhao-Wei Zhong521236.01