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Research Papers

Norris–Landzberg Acceleration Factors and Goldmann Constants for SAC305 Lead-Free Electronics

[+] Author and Article Information
Pradeep Lall

Department of Mechanical Engineering, NSF-CAVE3 Electronics Research Center, Auburn University, Auburn, AL 36849lall@eng.auburn.edu

Aniket Shirgaokar, Dinesh Arunachalam

Department of Mechanical Engineering, NSF-CAVE3 Electronics Research Center, Auburn University, Auburn, AL 36849

J. Electron. Packag 134(3), 031008 (Jul 19, 2012) (12 pages) doi:10.1115/1.4006863 History: Received December 25, 2010; Revised March 13, 2012; Published July 19, 2012; Online July 19, 2012

Goldmann constants and Norris–Landzberg acceleration factors for SAC305 lead-free solders have been developed based on principal component regression models (PCR) for reliability prediction and part selection of area-array packaging architectures under thermo-mechanical loads. Models have been developed in conjunction with stepwise regression methods for identification of the main effects. Package architectures studied include ball-grid array (BGA) packages mounted on copper-core and no-core printed circuit assemblies in harsh environments. The models have been developed based on thermomechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Packages with Sn3Ag0.5Cu solder alloy interconnects have been examined. The models have been developed based on perturbation of accelerated test thermomechanical failure data. Data have been gathered on nine different thermal cycle conditions with SAC305 alloys. The thermal cycle conditions differ in temperature range, dwell times, maximum temperature, and minimum temperature to enable development of constants needed for the life prediction and assessment of acceleration factors. Goldmann constants and the Norris–Landzberg acceleration factors have been benchmarked against previously published values. In addition, model predictions have been validated against validation datasets which have not been used for model development. Convergence of statistical models with experimental data has been demonstrated using a single factor design of experimental study for individual factors including temperature cycle magnitude, relative coefficient of thermal expansion, and diagonal length of the chip. The predicted and measured acceleration factors have also been computed and correlated. Good correlations have been achieved for parameters examined. Previously, the feasibility of using multiple linear regression models for reliability prediction has been demonstrated for flex-substrate BGA packages (Lall , 2004, “Thermal Reliability Considerations for Deployment of Area Array Packages in Harsh Environments,” Proceedings of the ITherm 2004, 9th Intersociety Conference on Thermal and Thermo-mechanical Phenomena, Las Vegas, Nevada, Jun. 1–4, pp. 259–267, Lall , 2005, “Thermal Reliability Considerations for Deployment of Area Array Packages in Harsh Environments,” IEEE Trans. Compon. Packag. Technol., 28 (3), pp. 457–466., flip-chip packages (Lall , 2005, “Decision-Support Models for Thermo-Mechanical Reliability of Leadfree Flip-Chip Electronics in Extreme Environments,” Proceedings of the 55th IEEE Electronic Components and Technology Conference, Orlando, FL, Jun. 1–3, pp. 127–136) and ceramic BGA packages (Lall , 2007, “Thermo-Mechanical Reliability Based Part Selection Models for Addressing Part Obsolescence in CBGA, CCGA, FLEXBGA, and Flip-Chip Packages,” ASME InterPACK Conference, Vancouver, British Columbia, Canada, Jul. 8–12, Paper No. IPACK2007-33832, pp. 1–18). The presented methodology is valuable in the development of fatigue damage constants for the application specific accelerated test datasets and provides a method to develop institutional learning based on prior accelerated test data.

Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Model development framework

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Figure 2

Scree plot for selecting the number of principal components

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Figure 3

Model adequacy checking for Goldmann model for Cu-core assemblies

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Figure 4

Plot of actual N1% life versus predicted N1% life for PBGAs on Cu core assemblies

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Figure 5

Model adequacy checking

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Figure 6

Plot of actual N1% life versus predicted N1% life for No Cu core dataset

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Figure 7

Model adequacy checking for Norris–Landzberg model for area-array packages

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Figure 8

Effect of ΔT on life of the package

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Figure 9

Weibull distribution of two temperature cycles

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Figure 10

Effect of half diagonal chip-size on cyclic life

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Figure 11

Weibull distribution showing the effect of chip-size on cyclic life

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Figure 12

Effect of αrel on life of the package

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Figure 13

Weibull distribution of two different αrel

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Figure 14

Effect of cyclic frequency on the acceleration factor for SAC305 solder interconnects

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Figure 15

Effect of temperature cycle magnitude on the acceleration factor for SAC305 solder interconnects

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