Thermal PreDimensioning
methodology based on
Thermal Impedance
03/18/2010
Patrick DUBUS
CIPS 2010 – Nuremberg/Germany
Renan LEON
Summary
Why do we need a new methodology ?
New methodology description
Transfer Function and Model Construction
Application Examples

Simple configuration for method validation

Power Module for Electrical Vehicle
Conclusion
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Why do we need a new methodology
Two mains reasons

Quick response expected to various configuration during early design
phase

Need to address complex profiles to be able to deal with real situation
What we need and what we can accept:

Reduced computation time (few seconds)

Results limited number of points (the critical ones)

Reduced accuracy (error < 5°C versus CFD tool).

Possibility for the electronic designer to do analysis by himself
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New Methodology Description (1)
Complete system reduced to a limited number of points:
  Power
injection points (m)
  Observed
Temperature points (n)
P1
P2
System
T1
T2
Pm
Tn
System considered linear:
  This
allows to say that temperature at point i is the linear combination of
the response to each power injection point (1 to m)
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New Methodology Description (2)
System is fully described when we know all the
individual transfer functions sij (n x m)
The easiest way for sij extraction is to derive the step
response (between injected power at point j and
temperature at point i)
In thermal domain step response correspond to Zth
(thermal impedance) but Zth is just a way to get the
transfer function
As this is a generic method, it can be used in many other
domains
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Transfer Function Extraction
Depends on project status:
  For
Project in design phase
–  CFD Simulation model is build
–  Number of run is equal to number of power injection point (m)
–  These are transient runs with step injected power (relatively simple)
  For
Project with existing hardware
–  CFD simulation or
–  Measurements using thermal sensor, IR camera, …
Temperature environment and Injected power during
CFD simulation or test shall be close to real
configuration (linearization at operating conditions)
One more simulation run or test added with complex
power injection profile for model validation (as described
after)
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“0D” Model Construction
Model depends on the method used for temperature
profiles calculation
Two options possible:
  Temperature
predicted using mathematical software (Scilab)
–  Pure mathematical model needed
  Temperature
predicted using SPICE simulation software
–  Assembly of RC network circuit needed
Use of SPICE simulation needs to add a calculation step
to define the equivalent RC networks with potential
additional errors
  Two
models are always generated to identify amount or errors produced
at each step (due to system linearization then to RC model generation).
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Methodology Flow Diagram
Transfer Functions
Extraction
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Construction of
Mathematical
“0D” Model
Construction of
SPICE
“0D” Model
Mathematical
Simulations
SPICE
Simulations
CFD Simulation
Comparison
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Several tools / Several Teams
Mix Between several Tools:
  CFD
Simulations or Tests for transfer functions extraction
  Mathematical
(Scilab) and/or SPICE models for temperature prediction
Collaboration by several teams:
  Thermal
CFD Simulation expert for transfer function extraction
  Electronic
Designer to run various configuration or complex profiles
  Improve
communication between the two teams for equipment
optimization
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Application Example 1
Simple configuration for method
validation

3 x D2PAK mounted onto PCB

PCB in still air
(potential non linear model)
1
3
2
Model reduced to:

3 injection points (the 3 D2PAK)

3 observed points (the 3 junction temperatures)
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SPICE Model
Mathematical
Model (Scilab)
Complex Profile
Application Example 1
  Blue
= « 0D » Model / Red = CFD Simulations
Errors limited to about 3°C
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Application Example 2
HOPE Power Module used in
traction inverter

Half Bridge

6 IGBT / 6 Diodes

1 CTN for temperature sensing
Model reduced to:

12 power injections points (IGBT + Diodes)

13 observed temperature points (12 dice + 1 CTN)

13 x 12 transfer functions to extract (Zth)
Model needed to predict junction temperature profile
for life driving cycles. Results used to define the
reliability tests and to make reliability predictions
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1- Driving Cycles
2- Power Losses
Application Example 2
« 0D » Model
4- Temperature cycles histogram
3- IGBT/Diode/Package Temperature
profiles
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Conclusion
New methodology by speeding up thermal simulation
allows:
  To
consider thermal constraint in design trade off and
  To
address real situation (complex profile) for design validation and
optimization
Thermal model (“0D” model) is build by considering only
limited number of point and a linear system
  Model
can be pure mathematical or equivalent SPICE circuit:
  The
two models are generated during model construction to control the
level and the origin of the errors
  SPICE
model offers to the electronic designer the capability to run thermal
simulations
Model generation fully automated now and used more and
more on various VALEO projects.
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