Design tools and Simulations for
Power Electronics Packaging
Dr Hua Lu
University of Greenwich
Power Electronics Centre Annual Conference 5-6 July 2016
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CMRG Group at Greenwich
• CMRG: Computational Mechanics and Reliability Group
• Working on power electronics:
– Chris Bailey, Hua Lu, Pushparajah Rajaguru, Catherine Tonry,
Mohammad Shahjalal
• Ongoing power electronics research activities
– UPE Components integration theme
• WP CI4: Design Tools and Modelling
– T4.1 Design for electro-thermal management
– T4.2 Design for Reliability and Robustness
– T4.3: Virtual Prototyping Framework for Power Electronic Systems
– UPE cross cutting topics
• Press-Pack, LED drivers, Multi-domain optimization
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Physics of Failure Based
Design and Reliability Analysis Tool
Select Component
FEA
• Geometry
• Material
properties
Electro-thermal
Temperature
Data acquisition
• Clech
• Analytical
• Damage
mech.
• Surrogate
model
• Passives
Power Electronics Centre Annual
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System
Reliability
Risk Analysis
Risk
Analysis
Damage &
Lifetime
3
Failure Mechanisms To be Considered
S. Yang, A. Bryant, P. Mawby, D. Xiang, L. Ran and P. Tavner, "An
Industry-Based Survey of Reliability in Power Electronic Converters,"
in IEEE Transactions on Industry Applications, vol. 47, no. 3, pp. 14411451, May-June 2011.
Types of failure mechanisms
• Semiconductor device level
• HCI, TDDB
• Passive components
• Changes in capacitance,
ESR, leakage current
• Inductor winding insulation
break down
• Packaging failure
• Wirebond lifting/heel
cracking
• Solder joint degradation
• Die cracking
• Substrate
delamination/cracking
• First step is to focus on capacitor and power module failure
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Reliability Prediction Methods
• Primarily physics of failure based reliability prediction
– Identify failure mechanisms
• e.g. Crack in components and interconnect, degradation of dielectrics, etc.
– Understand the physical processes
• E.g. Electric currents and voltages, Fluid flow, heat transfer, diffusion,
electromigration
– Predict lifetime based on physical process analysis under given conditions
• E.g. critical crack length, ESR etc.
• Advantages
– More accurate than traditional “handbook” method
– Optimization at design stage
• Disadvantages
– Need lots of data and deep understanding of damaging processes
• Structure, Failure and lifetime model, Environment, Material properties
– Often complicated and time consuming
• Expensive experiments and specialised software tools and training for numerical
simulation
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Capacitors Failure Mechanisms
• Failures: high leakage, open, short,
ESR/capacitance drift
• Mechanisms: cracking, assembly
and handling damage, electrolyte
drying, electro-chemical migration,
corrosion …
Aluminium Electrolytic capacitors Failure rates:
 p  b CV  Q E
Acceleration factors: capacitance, quality,
environment
Source: NAVSEA
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Capacitor Lifetime Models
Aluminuim electrolytic capacitor
 Vr  Tm T10a T 
L  L0   2
 V0 
n
Ta, Tm are ambient and maximum
temperature rating, ΔT is the
temperature rise from ripple current. Vr
and V0 are voltage rating and applied
voltage. L0 is load life rating, n =1 and 7
for aluminium electrolytic and film
capacitor respectively.
Ceramic capacitors
 Vr 
L  L0  
 V0 
3
 Tm 
 
 Ta 
8
Lifetime models may also be written as :
• A primary reason for wear out in aluminium electrolytic capacitors
is due to vaporization of electrolyte and degradation of electrolyte
due to ion exchange during charging/discharging
• In a physics based model, volume needs to be tracked and used in
lifetime prediction:
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Solder/Wirebond Lifetime Models
• Coffin Manson Lifetime model
• Energy based
• Strain based
Nf -Number of cycles
N f  AT  b
N f  Af  a T  b expEa / RTmax 
N f  aW pb
N f  a bp
W p -Plastic work density/cycle
 -Plastic
strain range/cycle
p
0.9
W p    ij d
t
ij
p
 p 
t 

t
They are correlated fatigue lifetime and
are sometimes called “damage indicators”
Plastic work density
0.8
2
d p : d p
3
W p
0.7
0.6
Wp(MJ/m3)
t 
0.5
0.4
0.3
0.2
0.1
0.0
0
2,000
4,000
6,000
8,000
10,000
12,000
t(s)
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Single and Multiple Stress level Loading
• Rainflow algorithm for cycle counts at different stress levels
Miner’s Damage
accumulation law
if
N fi  Wi

D
1
k
n W 


i 1
i
i
This damage can be used to predict lifetime (based
on the loading or given future loading) data
Power Electronics Centre Annual
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Rainflow cycle counting
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Analysis: Surrogate Models
• Identify design variables (DVs)
• Use DOE to find points in the design space
• carry out FEA simulation to obtain damage/lifetime for
each design.
• Surrogate models are obtained by fitting simple functions
to the results
W  W  X , X , X     X X    X  
Example with 3 DVs
3
1
2
3
i  j 1
3
ij
i
j
i 1
i
i
E p  0.0092  0.00416 x1  1.00 1006 x2  0.000445 x3  2.50 10 07 x1 * x2  0.00027 x1 * x3
 5.00  1007 x2 * x3  0.001914 x1 * x1   1.99 10 05 x2 * x2  0.00015 x3 * x3
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Stress Analysis: 3D -> 2D
• 2D FEA simulation is much faster than 3D
• But
– Not always a good approximation
– Inaccurate results
• Can 2D modelling be used for lifetime prediction and
design optimization? What are the limitations?
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3D, 2D Comparison
0.6
ΔW(MPa)
0.5
0.4
0.3
0.2
0.1
0
A
VM stress and deformation show
significant difference
B
C
D
E
F
A.
B.
C.
D.
Plane strain
Axisymmetric
3D slice model with two sides fixed
3D slice model with symmetry boundaries on one
side and the other is coupled
E. 3D slice model with one symmetry plane and the
other side is free
F. 3D Model
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2D FEA for Trend & Lifetime Prediction
0.9
ΔW3D(MPa
0.8
0.7
0.6
0.5
0.4
0.3
0.19
0.21
0.23
0.25
ΔW2D(MPa)
0.27
0.29
• ΔW values are different for 3D and 2D models
• ΔW3D and ΔW2D is linearly related (Plane strain 2D): ΔW3D =5.25ΔW2D-0.812
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Stress Analysis: Clech Algorithm
•
•
•
•
Stress states are described by τ and γ
Time dependent temperature loading
Temperature increases step by step
At each time step stress and strain evolves along
a stress reduction line
 12 
DT2  T0    1 / GT    1
1 / K  1 / GT 
 12   12 / K  DT2  T0 
 2   12  crt
 2   2 / K  DT2  T0 
Stress reduction line
   / K  DT  T0 
Hall, P. M., 34th Electronic Components Conference, New Orleans, LA, May 14-16, 1984, pp. 107-116.
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Clech Algorithm Results
   / K  DT  T0 
• FEA is used to simulate the stress and strain
• Extract the average stress and strain at the
edge of solder joint model.
• Fit the stress reduction line equation to the
data.
• Mean values of K and D at different
temperature is used.
450
400
T(K)
350
300
250
200
0
5000
10000
Stress-strain for a range of solder thickness
t(s)
Regular temperature loading
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Clech vs. FEA for Different dT
Temperature loading:
•
•
•
Effect of temperature range on
damage
Trend quite similar
Approximate linear relationship
0.35
0.18
0.30
0.16
ΔWClech(MPa)
ΔW(MJ/m3)
0.20
0.15
Clech
0.10
Tmax(C)
Tmin (K)
Tmax(K)
dT
-40
125
233.15
398.15
165
-25
110
248.15
383.15
135
-10
95
263.15
368.15
105
5
80
278.15
353.15
75
y = 0.7033x - 0.0379
0.14
FEA
0.25
Tmin(C)
0.12
0.1
0.08
0.06
0.04
0.05
0.02
0
0.00
50
70
90
110
130
150
170
190
0.00
ΔT
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0.05
0.10
0.15
0.20
0.25
0.30
0.35
ΔWFEA(MPa)
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Global CTE Mismatch
• Effect of baseplate CTE
• Substrate CTE: 7.5x10-6/°C
Substrate CTE=10ppm/K
• As global CTE mismatch diminishes,
Clech Algorithm seems to fail
Power Electronics Centre Annual
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New. Time Domain Damage Model
Damage rate:
• New features
Visco-plastic strain and microstructure evolution:
– Effect of plastic strain
distribution
– Effect of damage
history
– Effect of temperature
Crack length vs. cycles
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PowerLife GUI
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Summary and Future Work
• Fast physics of failure reliability analysis tool
• Stress analysis and lifetime models implemented to predict
wirebond/solder failures
• Future work
– Continued development of the software
– Collection of data and models
– New stress analysis functionalities for IGBT and other
semiconductor devices
– Capacitor analysis and physics of failure lifetime model
– Adding design optimization functionality
– System level optimization for reliability linking in with DTM cross
cutting projects
– More integration with external design and analysis software
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Conference 5-6 July 2016
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