1. Characteristics Simulation for a VSI system UoD 04/12/2008

2. Aims of study
A simulation study based on semiconductor devices physical models is carried out for a voltage-source-inverter (VSI) to examine the effects of device degradation on the converter thermal and electrical characteristics.
It’s hoped that this work can lead to the development of a condition monitoring (CM) technique for power electronic systems.
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3. Failure Mode, Mechanism and Effect Analysis (FMMEA) of IGBT
Cases studied
Failure modes
Failure causes
Failure mechanisms
Failure effects represented in study
Case 1:
Solder fatigue
Temperature swings
Different CTEs
Thermal gradients
Thermal resistance (Rjc) increases 20%
Case 2:
Gate degradation
High temperature
High electric field
Over voltage
High current density
Time dependant dielectric breakdown
Hot carries
Gate threshold voltage (Vt) decreases 20%
Case 3:
Bond-wire lifting
Number of chips in parallel decreases
(e.g. 8=>7)
Failure Mode, Mechanism and Effect Analysis (FMMEA) of IGBT
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4. Saber physic-based IGBT and diode models
Advantages:
Detailed physic model
Electrothermal simulation available
Experimentally verified
Disadvantages:
Difficult to parameterize
Encryption of MAST template
Hefner
IGBT model [1]
H. A. Mantooth
Diode model [2]
[1] A. R. Hefner and D. M. Diebolt, “An Experimentally Verified IGBT Model Implemented in the Saber Circuit Simulator,”
Proc. of IEEE Power Electronics Specialists Conference, Cambridge, MA, 1991.
[2] H. A. Mantooth, R. G. Perry, J. L. Duliere, “A unified diode model for circuit simulation,” IEEE Trans. On Power
Electronics, vol. 12, No. 5 pp , September,
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5. Diode Electrical Parameters Diode thermal Dependent Coefficients
Diode parameters [3]
Diode Electrical Parameters
Description
ISL = 1.25 mA
Saturation current for low level recombination
NL = 3.89
Emission coefficient for low level injection
BV = 600 V
Breakdown voltage
CJO = 3 nF
Zero-bias junction capacitance
TSW = 130 ns
Charge Sweep out time
TT = 165 ns
Carrier Lifetime
Tm=20ns
Charge diffusion time
Diode thermal Dependent Coefficients
XTI = -2.36
ISL temperature exponent
TNL1 = E-4
Linear NL temperature coefficient
TNL2 = E-6
Quadratic NL temperature coefficient
BETA = 1.71
Temperature exp. of carrier lifetime TT
BETASW = 1E6
Temperature exp. of carrier lifetime TSW
TRS1=3.12E-3
Linear RS temperature coefficient
TRS2=1.39E-5
Quadratic RS temperature coefficient
SKM100GB063D
Diode parameters
(H. A. Mantooth model)
[3] John Vincent Reichl, “Inverter Dynamic Electro-Thermal Simulation with Experimental Verification”, M.sc Thesis,
Virginia Polytechnic Institute and State University, 2005
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6. IGBT Electrical Parameters IGBT thermal Dependent Coefficients
IGBT parameters [3]
IGBT Electrical Parameters
Description
A = 1cm2
IGBT chip Area
Isneo = 1.5 x A
Emitter electron Saturation Current
Wb = .015 cm
Metallurgical drift region width
VTO = 6.4 V
MOSFET channel threshold voltage
Nb = 1.5 x 1014cm-3
Base dopant density
Kplino = 23 A/V2
Linear region transconductance
Kpsato = 11.5 A/V2
Saturation region transconductance
τHLO = .651 μs
High level minority carrier lifetime
Cgs = 3.98 nF
Gate-source capacitance
Coxd = 15.1 nF
Gate-drain overlap oxide capacitance
IGBT thermal Dependent Coefficients
τHL1 = -2.36
High Level minority carrier lifetime temp. coeff.
Isne1 = 1.189
Emitter electron saturation current temp. coeff
VT1 = -7.2mV/K
MOSFET channel threshold voltage temp. coeff.
Kplin1 =
Linear region transconductance temp. coeff
Kpsat1 =
Saturation region transconductance temp. coeff.
SKM100GB063D
IGBT parameters
(Hefner model)
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7. Thermal model and parameters [4]
IGBT Zth(j-c)
Values
Diode Zth(j-c) R1
160m k/W
400m k/W R2
88m k/W
165m k/W R3
18m k/W
30.5m k/W R4
4m k/W
4.5m k/W τ1 s s τ2 s s τ3 s s τ4 s s
4-order Forster RC
thermal model for
SKM100GB063D [4]
[4] Datasheet of SKM100GB063D, SEMIKRON, 2006
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8. Electrothermal calculation for comparison
Thermal calculation results are in close agreement with our simulation results, as shown next.
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9. Solder fatigue simulation (case 1)
(a) Junction temperatures
Tj_t ↑15C , Tj_d ↑1C
Tc ↑
Change of case temperature detectable!
(b) Current harmonics
Ia_5th ↓5.66 mA
Ua_5th ↓9.4 mV (↓0.8%)
Small!
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10. Solder fatigue simulation (case 1)
(c) On-state resistances
Ron_t ↑0.91 mΩ (↑2.42%)
Ron_d ↑0.01 mΩ (↑0.09%)
Small!
(d) dv/dt
td_off ↑ 13 ns
td_on little change
Small!
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11. Gate degradation simulation (case 2)
(a) Junction temperatures
Tj_t and Tj_d: little changes
(b) Current harmonics
Ia_5th ↓38.1mA
Ua_5th ↓63.3 mV (↓5.3%)
Perhaps Detectable
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12. Gate degradation simulation (case 2)
(c) On-state resistances
Ron_t ↓1.17 mΩ (↓3.12%)
Ron_d ↑0.006 mΩ (↑0.05%)
(d) dv/dt
td_off ↑ 80 ns
td_on ↓ 13 ns
Notable!
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13. Bond-wire lift simulation (case 3)
(a) Junction temperatures
Tj_t ↑5C , Tj_d: little change
(b) Current harmonics
Ia_5th ↑29.0mA
Ua_5th ↑48.2 mV (↑4.1%)
Perhaps Detectable
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14. Bond-wire lift simulation (case 3)
(c) On-state resistances
Ron_t ↑3.526 mΩ (↑ 9.12%)
Ron_d ↑0.007 mΩ (↑0.05%)
Detectable
(d) dv/dt
td_off ↓54.0 ns
td_on: little change
Perhaps detectable
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15. Results and discussions
Simulation results show that there are changes of converter electrical and thermal characteristics due to the device degradation. With respect to CM, some points can be made as below:
Solder fatigue mainly influences the thermal behaviour of converter, while it has very small effects on the converter terminal electrical characteristics. It’s suggested that thermal characteristics based CM method might be suitable for detection of solder fatigue.
Gate degradation mainly influences the electrical characteristics, especially the harmonics and dv/dt.
Bond-wire lifting mainly influences the electrical characteristics, especially the harmonics and On-state resistances.
Electrical characteristics based CM method might be more suitable for bond-wire lifting and gate degradation failures. However, high sensitive signal detection method is essential for the techniques.
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16. Targeted CM methods Thermal resistance observation based CM
The accuracy of Rth observation under various operating conditions is important.
Characteristics harmonics identification based CM
About 0.1% precision could be achieved according to our preliminary research.
On-state resistances identification based CM
The change of converter terminal resistance can be identified according to the machine’s electromagnetic transient characteristics, when it is comparable to the machine winding resistance.
EMI characteristics based CM
The dv/dt of output PWM voltage will influence the converter EMI characteristics. It is expected that the change of high-frequency characteristics could be used for failure detection.
A structured combination of different techniques might be appropriate in practice!
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17. Thanks for you attention!