Response surface models for efficient, modular estimation of solder joint reliability in area array packages

Abstract In package development as well as package use environments, the packaging engineer is often called to estimate, quickly and accurately, the impact of various design/process parameters on the solder joint reliability. The usual, laborious approaches commonly adopted for this estimation are complete finite element analysis or extensive environmental stress testing. As an alternative to these approaches, in this paper, we develop and evaluate response surface models based both on feed-forward, back-propagation neural networks as well as linear regression models for predicting (in near real-time) the fatigue life of solder joints in area array packages. The models are valid for most solder joints used in chip scale packages (CSP) and fine-pitch ball grid array (FBGA) packages. There are two physical models which are executed in sequence as part of the analysis procedure. The first is a droplet shape prediction code developed as part of this project, which predicts the shape of the solder joint given the input pad sizes, package weight and mask definition, and the second is a commercial nonlinear finite element analysis code, which determines the inelastic dissipation for a given shape. The predicted inelastic dissipation is then used to determine the fatigue life of the joints. The response surface models developed in this study are shown to perform very well in capturing the non-linear relationship between the inputs and output, with a loss in accuracy of less than 5%. Also, using the same training data, the linear regression models are shown to be marginally better in accuracy than the neural network models. The response surface models such as the ones developed in this paper are expected to be an important part of modular, decomposed analysis procedures developed by the authors in other research.

[1]  M.T.W. de Langen.  Low Cost Flip Chip Technology , 1997 .

[2]  J. Lau,et al.  Solder Joint Reliability of BGA, CSP, Flip Chip, and Fine Pitch SMT Assemblies , 1996 .

[3]  John H. Lau,et al.  Chip scale package (CSP) : design, materials, processes, reliability, and applications , 1999 .

[4]  Li Zhang,et al.  An evaluation of back-propagation neural networks for the optimal design of structural systems: Part I. Training procedures , 2002 .

[5]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[6]  John S. Corbin,et al.  Finite element analysis for Solder Ball Connect (SBC) structural design optimization , 1993, IBM J. Res. Dev..

[7]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[8]  Ganesh Subbarayan,et al.  Decomposition Techniques for the Efficient Analysis of Area-Array Packages , 2000 .

[9]  Li Zhang,et al.  An evaluation of back-propagation neural networks for the optimal design of structural systems: Part II. Numerical evaluation , 2002 .

[10]  C. Harper.  Electronic Packaging and Interconnection Handbook , 2000 .

[11]  Ganesh Subbarayan,et al.  A System for First Order Reliability Estimation of Solder Joints in Area Array Packages , 1998, Manufacturing Science and Engineering.

[12]  Nicholas J. Nigro,et al.  Parametric Finite Element Method for Predicting Shapes of Three-Dimensional Solder Joints , 1996 .

[13]  C. P. Wong,et al.  Reworkable underfills for flip chip, BGA, and CSP applications , 2000, 2000 Proceedings. 50th Electronic Components and Technology Conference (Cat. No.00CH37070).

[14]  Li Zhang,et al.  The accuracy of structural approximations employed in analysis of area array packages , 1999 .

[15]  Kuo-Ning Chiang,et al.  An Effective Approach for Three-Dimensional Finite Element Analysis of Ball Grid Array Typed Packages , 1998 .

[16]  Roop L. Mahajan,et al.  Reliability Simulations for Solder Joints Using Stochastic Finite Element and Artificial Neural Network Models , 1996 .

[17]  Ephraim Suhir,et al.  Mechanical Behavior of Flip-Chip Encapsulants , 1990 .

[18]  Ganesh Subbarayan,et al.  Predictive reliability models through validated correlation between power cycling and thermal cycling accelerated life tests , 2002 .

[19]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .