level, using a fatigue curve generated at the component level
is presented as well.
Setups and Methodology
Component-Level Test Setup
A Dynamic Test Board (DTB) was designed and tested
with an electro-dynamic vibration shaker. The DTB, shown in
Figure 1(a), is an approximately 1.57mm thick, 305mm2
square board with only a BGA package mounted on it in the
center. It was made up of typical FR4 printed circuit board
material. The holes on the board, arranged in a circular
pattern with constant distance, were used to secure the board
onto a support plate using standoffs. The multiple holes
enable obtaining different bend modes using the same board,
by just changing the mount locations appropriately. The fine
pitch BGA components were designed with a full daisy chain
array of either lead-free with tin-silver-copper (SnAgCu) or
leaded (SnPb) solder balls. Figure 1(b) shows the test board
assembly on an electro-dynamic vibration shaker.
Figure 1: Vibration test setup: (a) a DTB mounted onto a fixture using eight
standoffs, and (b) complete picture of the setup.
The potential failure modes in vibration loading are: (i)
solder joint crack on board and/or package side, (ii) PCB
trace crack close to package outer corners (especially when
trace thickness is relatively small compared to the pad
diameter), and (iii) pad crater on board side (normally with
metal-defined pads). In order to focus on solder joint failure
only, the other two failure modes were eliminated by adding
the following design features in the corner areas of the board:
thicker traces, redundant traces, and solder-mask defined
(SMD) pads to ensure only BGA fatigue failure in all of the
test boards. More detail on the impact of board design can be
found in Malatkar, et al .
Under harmonic input, the board response would
typically contain significant contribution from one or more
modes. These are due to nonlinearities inherent in the system.
In order to obtain a clean sinusoidal response, it would
become necessary to minimize the impact of nonlinearities.
For inputs beyond 5g (gravity acceleration), the nonlinear
response was prominent, and hence the input g-level did not
exceed 5g. Also, to avoid interaction between modes through
internal resonance, the location of the standoffs (used to
mount the board onto the shaker) was varied until the ratio of
the first and second/third natural frequencies was away from
1:2 and 1:3 .
As discussed earlier, it is not feasible to characterize
fatigue failure of solder joints using the S-N curve approach
due to difficulty in measuring the solder joint stress during
vibration. Instead, the board strain which is indicative of the
severity of the solder joint stressing was used. Therefore, a
board strain versus cycles-to-failure plot (or E-N curve) was
developed. A 45° strain gage rosette was placed on the
secondary side of every test board, underneath the package
corner, to measure the localized bending strain as shown in
Figure 2. Due to slight variations in the natural frequency and
therefore the amplification of individual boards, it was
necessary to directly measure the strain for every board tested.
Each DTB was subjected to a sinusoidal input till failure by
having a fixed G-level at a resonant frequency. The number
of cycles to failure was calculated through the time to failure
(in seconds) multiply with the input frequency (in Hz). Input
G-levels from 0.75g to 4.0g were used to produce a range of
board strain values. To monitor the connectivity of the solder
joints, resistance of daisy chain structure in the package and
board was recorded. A 100% increase in resistance was
indicative of failure in one of the solder joints, as shown in
Figure 3. The compressive principal strain (corresponding to
tensile stress in solder joints) and number of cycles to failure
data collected from all of the test boards was used to generate
the E-N curve.
Figure 2: Strain gage attached close to the package corner on the secondary
side of board.
Figure 3: Electrical daisy chain in-situ monitoring of two different boards
with different failure times (100% increment in electrical resistance).
374 2007 Electronic Components and Technology Conference
FEA Model Setup
A detailed 3-D model consisting of the DTB, BGA
package and solder joints was modeled using ABAQUS*, a
commercially available FEA software. Specifically, the
component-level test setup (reference to Figure 1), was
modeled and shown in Figure 4. The model was assembled
using appropriate constraints and coupling equations .
Based on the high-cycle fatigue failures observed
experimentally, linear material properties were assumed. The
specific values used for the various material parameters were
similar to the ones used by Loh and Garner . In this study,
the linear elastic, modal superposition method was adopted, as
it was computationally efficient and yet yielded accurate
results. The board and the package were optimally meshed
using linear and quadratic elements. Mesh density was
appropriately set to provide adequate resolution at locations
of interest. A local model, illustrated in Figure 5, was also
analyzed to verify the local solder joint stresses.
For validation and verification purposes, two different
analyses were performed: harmonic and steady-state dynamic
analyses. The former determines the model’s dynamic
response as a function of frequency while the latter
determines the steady-state time response at a predetermined
excitation frequency. These analyses were preceded by a
frequency extraction step that determines the natural
frequencies and mode shapes in the frequency range of
interest. Modes up to twice the highest frequency in the sweep
range were extracted to ensure the contributions of higher
modes were accounted for in the superposition technique.
Figure 4: FEA model of the DTB, BGA package and boundary conditions.
Figure 5: Solder joint used in: (a) global model and (b) local model.
System-Level Test Setup
An ATX form factor mechanical test board (MTB), as
illustrated in Figure 6 was used for system level validation.
By benchmarking DTB, the MTB also has the similar design
features to ensure only solder joint failure is detected. Since
the same BGA component was tested on DTB level, this BGA
package was the only component monitored for the system
level fatigue life prediction methodology validations. The
accompanied heat sink of the component was purposely
removed to allow meaningful comparison with DTB can be
done at later stage. However, all other components (CPU with
heat sink, connectors, etc.) were included on overall MTB
assembly. A fixture plate mimicking an ATX chassis was
designed and used for system level vibration testing. The
board thickness used is approximately 1.57mm and assembled
with SnAgCu solder system. Similar to DTB, board strain and
daisy chain resistance were the two main metrics monitored.
Initially, strain was measured at all four corners of the MCH.
Due to the non-symmetric response induced by the proximity
to the CPU thermal solution, one corner was found
significantly higher consistently. Hence, for all subsequence
tests, only the worst case corner was monitored.
Figure 6: A fully-loaded MTB secured to a fixture plate, prior to mounting
onto the vibration table.
The fatigue life prediction methodology is based on the
Miner’s cumulative damage theory, which accounts for the
damage accumulated over every stress cycle. As the boards
are tested with random input, a methodology is needed to
determine their strain components and also the number of
occurrences (or cycles). To achieve this, rainflow cycle
counting algorithm was used, which essentially determines
the number of cycles ni at each strain level εi from the
irregular strain history [16 &17]. Miner’s rule was then
applied to determine the cumulative damage from the various
strain components. Failure occurs when the cumulative
damage equals (or exceeds) C, as given below:
where ni, obtained from the rainflow algorithm, corresponds
to the number of cycles of the strain component εi in the board
response and Ni, obtained from the E-N curve at the strain
375 2007 Electronic Components and Technology Conference
level εi. C was experimentally found to be between 0.7 and
2.2, but for design purposes, it is usually assumed to be 1.
Results and Discussions
Component-Level Analysis: Fatigue Curves
The fatigue E-N curves were generated under vibration
input using 45 test boards with SnAgCu BGA packages and
31 test boards with SnPb solder. The resultant E-N curves are
plotted together in Figure 7. On a log-linear plot, all of the
data points of a given solder material fall along a straight line,
with a correlation coefficient (R2
) of around 0.8. These E-N
curves have characteristics of a classical high-cycle S-N curve
and, thus, demonstrated that the board strain is a viable metric
in studying solder joint fatigue life. The scatter in the data
points is not significant, which suggested that the test setup
and procedure were robust and provided repeatable data. It is
also obvious from Figure 7 that the high-cycle fatigue
performance of SnAgCu solder system under vibration
loading is much better than SnPb solder system.
Figure 7: E-N curves of SnAgCu and SnPb solder systems under vibration.
In addition to vibration, boards under cyclic bend and
cyclic shock loading conditions were tested. The detailed test
setups and input conditions were described in . Here the
vibration results along with those obtained from the cyclic
bend and cyclic shock tests were combined, to present a
complete picture of the mechanical fatigue characteristics of
SnAgCu and SnPb solder systems. For the case of SnPb
solder system (Figure 8), all of the data points from vibration,
cyclic bend and cyclic shock tests fell along a straight line in a
log-log plot, with the R2
being 0.92. However, the SnAgCu
data fell along two distinct lines: vibration and cyclic bend
data along one line (R2
= 0.88) and the cyclic shock data on
another line (R2
= 0.76), as illustrated in Figure 9. The reason
for the two sets of lines seen in the fatigue behavior of
SnAgCu system is potentially due to the different failure
modes observed in cyclic bend and cyclic shock tests. The
failure mode transitions from predominantly bulk solder
failure in cyclic bend to completely inter-metallic compound
(IMC) failure in cyclic shock. In other words, the strain rate
seems to be dictating the failure modes and their transitions,
as reported in . This could be due to the dependency of
bulk solder (ductile material) and IMC (brittle material)
strength on the strain rate. At higher strain rates, tensile
strength of bulk solder seems to be higher than IMC, and
therefore, there was a transition in failure mode. For more
details on these failure analysis finding and discussions, refer
Figure 8: Mechanical fatigue curve of SnPb solder system.
Figure 9: Mechanical fatigue curves of SnAgCu solder system..
Figure 10: Cross-over (as indicated by arrow) in the SnAgCu and SnPb
solder fatigue data points.
All the data points with the SnPb and SnAgCu solder
systems are plotted together in Figure 10. Interestingly, the
two sets of data points, with totaling 152 in number and
spanning over ten million cycles to failure, cross over
approximately between 1x102
ranges of the
376 2007 Electronic Components and Technology Conference
normalized cycles-to-failure point. Below the cross-over
point, comprising vibration and cyclic bend data, SnAgCu
solder has a higher number of cycles to failure than SnPb
solder. And above the cross-over point, comprising cyclic
shock data, performance of SnPb solder is much better.
Component-Level Analysis: Failure Analysis Results
Figure 11: Cross-section images of a solder joint indicating cracks on the
board and package interfaces, along with their initiation locations and
Figure 12: Dye-and-pry images of package and board interfaces on the four
corners solder joints. The dye material on board and package pads indicates
the presence of a crack and also its propagation direction.
All of the test boards went through failure analysis (FA),
to verify the actual failure modes. Two FA methods, namely
cross-sectioning and dye-and-pry (D&P), were used to
determine the crack initiation location and direction of crack
propagation under vibration loading. Majority of the samples
had predominantly bulk solder failure, but in certain cases the
crack also propagated through the IMC layer, especially in
SnAgCu solder system. The common trends observed are as
follows: (i) The crack always initiated near to bulk solder,
right next to the solder resist, due to the high stress
concentration generated at that location; (ii) The crack then
propagated through the bulk or along the solder/IMC interface
and/or through the IMC layer; and (iii) The direction of crack
growth was along the package diagonal. On the board side,
the cracks initiated and grew towards package center, whereas
on the package side it grew away from package center. All
these trends are illustrated in the cross-section images in
Figure 11 and the D&P images in Figure 12. This cracking
behavior was further validated with FEA approach in the next
FEA Model Analysis: Model Tuning and Validation
The FEA model was correlated for global behavior using
experimental frequency-response function (FRF). The model
was tuned based on the data collected by the accelerometer
placed at the center of the BGA package. Figure 13 shows the
FRF comparison between experiment results and FEA
predictions. As tabulated in Table 1, a relatively good
correlation between the natural frequencies with errors well
within 10%. However, the comparison discovered a missing
mode and minor disparity in the higher frequencies. These
differences were attributed to the boundary conditions
assumed in the model. For instance, the actual standoffs were
elastic and influence the board response, but were assumed to
be rigid in the FEA model.
The validation and tuning process also involved
comparing the mode shapes. The modes shapes from FEA
and experimental modal analysis (EMA) had remarkably
good correlation throughout the entire frequency range of
interest. Figure 14 shows the excellent graphical matching
between the 1st
modes of the DTB mode shapes.
Furthermore, the board strain response obtained from the
model with the experimental strain gage measurements for
various input G levels was compared. Figure 15 illustrates the
good matching of the in-plane board strain time history
between experiment and FEA for sinusoidal input of 1.0g in
time history plots.
Figure 13: Experimental and FEA FRF comparison at 0.1g sinusoidal input.
Table 1: Comparison between experimental and FEA natural frequencies
1 78.8 75.4 -4.35%
2 126.9 137.3 +8.17%
3 297.5 306.4 3.00%
4 328.5 - -
5 367.3 370.1 +0.78%
6 447.6 432.1 -3.47%
7 470.3 462.3 -1.69%
8 487.5 500.3 +2.64%
within bulk solder
Crack initiation in
within bulk solder
Crack initiation in
377 2007 Electronic Components and Technology Conference
Figure 14: Mode shape matching between EMA and FEA for: (a) 1st
and (b) 5th
Figure 15: Comparison of DTB strain response between FEA prediction and
experimental values for a sine input of 1.0g.
FEA Model Analysis: Solder Joint Stress and FA Trends
As discussed in previous sections, fatigue fracture through
the bulk solder adjacent to the IMC is the common failure
found in solder joints subjected to vibration loading. Figure
16 shows the cross-section view of a typical solder joint
fatigue failure and the FEA prediction of the maximum
principal stress vectors on the board and package interfaces.
The FEA prediction of the crack initiation location and
direction of crack propagation matched exactly with that
observed in experimental FA results. In addition, the
maximum principal stress contour from FEA was in
agreement with D&P images in Figure 17. Again, similar
trends between experiment and FEA were observed.
Besides, the FEA model has established the relationship
between the in-plane board strain and the stress generated at
the corner most BGA solder joints. The board strain and
solder joint stress values were recorded for different
acceleration levels while keeping the input frequency fixed at
natural frequency. From Figure 18, it is clear that the
board strains and solder joint stresses are linearly correlated
and this was observed in . This linear relationship justifies
the use of board strain as a metric to study BGA package
solder joint fatigue and allow translation of the E-N curve into
S-N curve in future works.
Figure 16: Cross-section image indicating crack initiation and propagation
direction along with FEA prediction of maximum principal stress vector.
Figure 17: Matching between stress contour plots and dye-and-pry images
from FA on the package and board interfaces.
Figure 18: Relationship between maximum solder joint stress and in-plane
378 2007 Electronic Components and Technology Conference
Methodology Validation and System-Level Analysis
The methodology validation activity was divided into two
parts. Firstly, the same setup of the DTB was subjected to
random vibration input. The random power-spectral density
was kept constant over a wide range of frequency, where the
average acceleration (in root mean square, gRMS) level was
varied for different board samples to obtain different time to
failure values. All tests were run until failure as determined
by 100% change in the daisy chain resistance. Strain data was
collected at 1000 scans per second for the entire duration of
test. The second part of methodology validation activity also
used random input, but this time tested with the MTB
The rainflow distributions for every response strain
history obtained from the DTB and MTB random input tests
were determined using a MATLAB* script . The Miner’s
sum values obtained for the two different tests are tabulated in
Tables 2 and 3. The average Miner’s sum obtained from the
six DTB and eleven MTB boards are 1.13 and 1.55,
Table 2: Miner’s sum calculated using DTB strain response data.
Test Input gRMS Miner’s Sum
random input test
Table 3: Miner’s numbers calculated using MTB strain response data.
Test Input gRMS Miner’s Sum
random input test
To get an idea of the variation in the E-N curve data
points, Miner’s sum for those boards were computed using
the following formula:
M = (2)
where, Ntest denotes the actual number of cycles to failure for
a given DTB from the test and Ncurve represents the predicted
number of cycles to failure calculated using the measured
strain of the DTB in the E-N curve relationship. Interestingly,
the average Miner’s sum obtained from the 45 DTB boards
used in the E-N curve generation is 1.52.
A pictorial summary of the Miner’s sum from the three
sets of testing (DTB with sine input, DTB with random input,
and MTB with random input) is illustrated in Figure 19.
Apparently, the variation in Miner’s sum calculated using the
proposed methodology based on the data from the random
input tests of DTBs and MTBs was well within the variation
seen in the sine tests. This clearly demonstrates that the
proposed methodology is very effective in predicting fatigue
failure accurately at the system level.
Figure 19: Distribution of Miner’s sum for all test cases.
Besides, an interesting trend in all of the DTBs and MTBs
boards tested under random vibration was also observed. The
Miner’s sum was entirely influenced by the higher strain
components, even though their occurrence in the overall
response was much lesser than the smaller strain components.
Figure 20 shows a joined plot of the rainflow distribution and
Miner’s sum contribution of the various strain components.
The design implication of this is that if a system can be
designed to reduce largest and most damaging strain
components, then the BGA fatigue life can be greatly
increased. It was also observed that the Miner’s sum for
greater inputs (gRMS levels) was higher. This could be due to
the difference in the strain state (or shape) of the board
between the sine and random test cases. In addition, the
current Miner’s sum calculation is not scalable. In other
words, board strain data needs to be collected during the
entire duration of the test (resulting in a large data file). This
is being attributed to potential nonlinear response in the
random vibration tests, due to the presence of high strain
components. These issues will be addressed in future studies.
Figure 20: The rainflow distribution and Miner’s sum contribution plots.
379 2007 Electronic Components and Technology Conference
A component-level E-N curve approach was proposed to
characterize BGA package solder joint fatigue under vibration
input. The specific designed test setup led to a linear E-N
curve indicating the robustness of this metrology. This also
validated the use of board strain as a metric to study solder
joint fatigue. The linear relationship between the solder joint
stress and board strain, obtained through FEA, further
confirmed the use of board strain as an optimum engineering
metric to monitor the severity of solder joint stressing. FEA
was also successfully employed to explain the location of
crack initiation and direction of crack propagation, on the
board and package interfaces, as observed in experiments.
The comparison of SnAgCu and SnPb fatigue curves clearly
indicated the better performance of SnAgCu solder system
under high-cycle fatigue test. However, this trend was
reversed in low-cycle fatigue, where SnPb solder has superior
fatigue resistance. The cross-over of SnAgCu and SnPb
fatigue curves and the role played by strain rate in dictating
the failure mode were also demonstrated. A fatigue-life
prediction methodology, based on Miner’s rule was proposed
and validated with reasonably good results using MTBs
mimic to real motherboards. This methodology also holds the
potential to be used seamlessly between all mechanical
loading scenarios – Vibration, Cyclic Bend and Cyclic Shock.
For future work, extension of the E-N curve approach to
socket solder joints is planned. The fatigue-life prediction
methodology would be used to study the fatigue failure under
combined vibration, cyclic bend and/or cyclic shock loading
conditions. Additionally, the FEA model would be further
developed to identify package and board design parameters
that significantly impact solder joint fatigue life, and in turn
come up with design solutions to improve fatigue resistance.
Also included in the plan are some fundamental studies on
different solder metallurgy to reason out the trends seen in the
fatigue curves of SnAgCu and SnPb solder systems.
The authors would like to acknowledge the help received
from Troy Pringle, Lee Chek Loon, Lew Huey Ling, Jose
Chavarria, Rick Brewer and Tozer Bandorawalla in fatigue
curve generation, failure analyses and methodology
development. Special thanks to Luke Garner, Loh Wei Keat,
Rick Williams, Pardeep Bhatti and Intel management for their
support and encouragement.
1. Barker, D. et al, “Combined Vibrational and Thermal
Solder Joint Fatigue – A Generalized Strain versus Life
Approach,” Journal of Electronic Packaging, Vol. 112
(1990), pp. 129-134.
2. Solomon, H. D., “Low Cycle Fatigue of Sn96 Solder
with Reference to Eutectic Solder and High Pb Solder,”
Transactions of ASME, Vol. 113 (1991), pp. 102-108.
3. Guo, Z. and Conrad, H., “Fatigue Crack Growth Rate in
63Sn37Pb Solder Joints,” Journal of Electronic
Packaging, Vol. 115 (1993), pp. 159-164.
4. Chuang, C. M., Lui, T. S. and Chen, L. H., “The
Characterization of Vibration Fracture of Pb-Sn and Lead
Free Sn-Zn Eutectic Solders,” Journal of Electronic
Materials, Vol. 30 (2001), pp. 1232-1240.
5. Shi, X. Q. et al, “Low Cycle Fatigue Analysis of
Temperature and Frequency Effects in Eutectic Solder
Alloy,” International Journal of Fatigue, Vol. 22 (2000),
6. Liu, X. et al, “Experimental Study and Life Prediction on
High Cycle Vibration Fatigue in BGA Packages,”
Microelectronics Reliability, Vol. 26 (2006), pp. 1128-
7. Suresh, S., Fatigue of Materials, Cambridge University
Press (Cambridge, 1992), pp. 126-140.
8. Miner, M. A., “Cumulative Damage in Fatigue,” ASME
Journal of Applied Mechanics, Vol. 12 (1945), pp. A159-
9. Wong, T. E. et al, “Development of BGA Solder Joint
Vibration Fatigue Life Prediction Model,” 49th
Component Technology Conf, 1999.
10. Steinberg, D. S., Vibration Analysis for Electronic
Equipment, Wiley (New York, 2000).
11. Pang, H. L. J. et al, “Vibration Fatigue Analysis for
FCOB Solder Joints,” 54th
Technology Conf, 2004.
12. Malatkar, P. et al, “Pitfalls An Engineer Needs To Be
Aware Of During Vibration Testing,” Proc 56th
Electronic Components and Technology Conf, San
Diego, CA, May. 2006.
13. Nayfeh, A. H., Nonlinear Interactions, Wiley (New York,
14. ABAQUS User Manual (version 6.5), ABAQUS Inc.,
15. Loh, W. K. and Garner, L. J., “Solder Joint Reliability
Prediction of Flip Chip Packages Under Shock Loading
Environment,” InterPACK, San Francisco, CA, July.
16. Dowling, N. E., Mechanical Behavior of Materials, 2nd
Edition, Prentice Hall (New Jersey, 1999), pp. 404-408.
17. Annual Book of ASTM Standards, Vol. 03.01, ASTM E
1049-85 (Reapproved 1997), Standard practices for cycle
counting in fatigue analysis, 2003.
18. Pringle, T. D. et al, “Solder Joint Reliability of BGA
Package under End-User Handling Test Conditions,” to
be presented at 57th
Electronic Components and
Technology Conf, Reno, NV, May. 2007.
19. Darveaux, R. et al, “Interface Failure in Lead Free Solder
Electronic Components and Technology
Conf, San Diego, CA, May. 2006.
20. Nieslony, A., “Rain Flow Counting Method,”
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380 2007 Electronic Components and Technology Conference