15-1

Power Device Degradation Estimationby Machine Learning of Gate Waveforms
Hiromu Yamasaki, Koutaro Miyazaki, Yang Lo, A. K. M. Mahfuzul Islam, Katsuhiro Hata,Takayasu Sakurai, and Makoto Takamiya
The University of Tokyo, Tokyo, Japan
Abstract— The emitter resistance (RE), the junction temperature(TJ), the collector current (IC), and the threshold voltage (VTH)of power devices are key parameters that determine thereliability of power devices. Adding dedicated sensors tomeasure the key parameters, however, will increase the cost ofthe power converters. To solve the problem, power device
degradation estimation methods by the machine learning of gate
waveforms are proposed. Two methods are shown in this paper.
First, in order to detect the bond wire lift-off of power devices,
the estimation of the number of the connected bond wires using
the linear regression of two feature points extracted from thegate waveforms of a SiC MOSFET is shown using SPICE
simulations. Then, in order to detect the power device
degradation, the estimation of RE, TJ, IC, and VTH using theconvolutional neural network (CNN) with the gate waveformsof an IGBT for input is shown using both simulations andmeasurements.

Parameters forpower device reliability

TJ, VTHnRE
Bond wire
lift-off(a)VG

VG,MIN
VG,BENT

Keywords- CNN, Neural Network, IGBT, power device,degradation, reliability, gateTimeI. INTRODUCTION
Highly reliable power devices are required, because thepower devices are important components of power electronics
systems that support society. As examples of the powerdevices, an insulated gate bipolar transistor (IGBT) and a SiC
MOSFET are discussed in this paper. The most frequent causeof the long-term degradation of the IGBTs is the bond wire
lift-off due to the thermal cycles [1,2], where emitter bondwires are peeled-off from the pad. Fig. 1 (a) shows fiveimportant parameters related to the bond wire lift-off in IGBTs,
the number of the connected bond wires (n), the emitter
resistance (RE), the junction temperature (TJ), the collector
current (IC), and the threshold voltage (VTH). In order tomeasure the values of n, RE, TJ, IC, and VTH, however, adding
dedicated sensors for current, voltage and temperature will
increase the cost of the power converters. Several previous
papers tried to reduce the number of sensors. The estimationof IC for an IGBT using the collector-emitter voltage (VCE),
gate-emitter voltage (VGE), TJ, and VTH as input has been
investigated based on a neural network [3]. It is, however, very
costly to know these voltages and TJ beforehand using sensors
and only IC can be estimated. The estimation of TJ by
measuring the peak voltage of the gate drive voltage has been
proposed [4]. The estimation, however, is only for TJ and since
it does not use an AI-based approach, the estimation needs
very high precision of the peak voltage measurement without
noise, which is practically very difficult.
To solve the problems, two power device degradation
estimation methods by the machine learning of the gate
voltage (VG) waveforms are proposed in this paper. Fig. 1 (b)shows a schematic of the VG waveforms and two feature points(VG,BENT and VG,MIN), where the definitions will be shown later.
In Section II, as shown in Fig. 1 (c), in order to detect the bondwire lift-off of power devices, the estimation of n using the© 2020 The Japan Society of Applied Physics

ICVG(b)Linearregression

VG,BENT & VG,MIN

In

Out

Estimation of n(c)CNN
VG waveform

InOut

Estimation ofRE, TJ, IC, and VTH(d)Fig. 1. Overview of this paper. (a) Parameters related to bond wire lift-off in
IGBTs. (b) Schematic of VG waveforms and two feature points (VG,BENT andVG,MIN). (c) Estimation of n using linear regression of two feature points. (d)Estimation of RE, TJ, IC, and VTH using CNN with VG waveforms.

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linear regression of two feature points extracted from VG
waveforms of a SiC MOSFET is shown using simulations. In
Section III, as shown in Fig. 1 (d), in order to detect the powerdevice degradation, the estimation of RE, TJ, IC, and VTH using
the convolutional neural network (CNN) with VG waveformsof an IGBT for input is shown using both simulations andmeasurements.
II.

DETECTION OF BOND WIRE LIFT-OFF USING LINEAR
REGRESSION OF TWO FEATURE POINTSIn order to detect the bond wire lift-off of power devices,in this chapter, the estimation of n using the linear regressionof two feature points (VG,BENT and VG,MIN) extracted from VG
waveforms of a SiC MOSFET is shown. As shown in Fig. 1(b), VG,BENT is defined the inflection point of VG around VTH,and VG,MIN is defined the first minima of VG.

Table I Parameters for SPICE simulation.
NameValue

VDD

Supply voltage

300 V

Periodic measurements of VG waveform

LVDD

Parasitic inductance between diode andcapacitor

6 nH

Extract VG,BENT & VG,MIN and write to memory

LLOADLoad inductance

1.5 mHLD

Parasitic inductance between diode and drain

3 nH

RG

External gate resistance

2.5 Ω

LG

Parasitic inductance between gate driver andgate

6 nHn

Number of connected bond wires

RS

Parasitic resistance of bond wires (n = 3, 2, 1)

0.270 mΩ,0.405 mΩ,0.810 mΩ

LS

Parasitic inductance of bond wires (n = 3, 2, 1)

1.15 nH,1.72 nH,
3.44 nH

RLEAD

Parasitic resistance of lead of source

7.41 mΩ

LLEAD

Parasitic inductance of lead of source

3.55 nH

LGR

Parasitic inductance between ground of gatedriver and lead of source

3 nH

LVDDR

Parasitic inductance between lead and capacitor

VDRIVE

Gate driver voltage source

Power-up

Temperaturerise during
power-onNumber of measurements (m)m < m1m ≥ m1
Find regression line and write to memory

3, 2, 1

Periodic measurements of VG waveform
Extract VG,BENT & VG,MIN and calculate anomaly score
Anomaly score (AS)AS < ASTH

AS ≥ ASTH
Warning of bond wire lift-off
VG,MINR egreen lins s io15

Anomaly score
VG,BENT

Fig. 2. Flowchart of proposed detection method of bond wire lift-off.

VG (V)

.

LVDD
LLOAD
IL

10n=3n=2n=1
TJ = 75 ºC
IL = 5 ALG

VG00TJRSnLS50100
Time (ns)

VDD

150

Fig. 4. Simulated VG waveforms with varied n at TJ = 75 ºC and IL = 5 A.

15RLEAD
LLEAD

10LVDDR

Fig. 3. Circuit schematic of double pulse test for SiC MOSFET.

Fig. 2 shows a flowchart of the proposed detection methodof the bond wire lift-off. During the initial power-on of apower converter, in order to collect two feature points withdifferent temperatures, VG waveforms are periodicallymeasured m1 times, VG,BENT and VG,MIN are extracted, and they
are written to memory. Then, a regression line is found using
the data of VG,BENT and VG,MIN, and the regression line iswritten to memory. After that, during the normal operation ofthe power converter, VG waveforms are periodically measured,VG,BENT and VG,MIN are extracted, and an anomaly score iscalculated. The anomaly score is the distance between the twofeature points and the regression line. When the anomaly scoreis larger than the predetermined value (ASTH), the bond wire
lift-off is detected.
Fig. 3 shows a circuit schematic of the double pulse test
for the SiC MOSFET (C3M0060065D, 650V, 37A) todemonstrate the estimation of n using the linear regression ofVG,BENT and VG,MIN. Table I shows the parameters for the336

VG (V)

LGR

0.22 V
0.52 V

VG,MIN+VDRIVE

Simulated

5LD
RG

6 nH0 V / 15 V

TJ = 25 ºC
TJ = 75 ºC
TJ = 125 ºC

n=3IL = 5 A

Simulated

50.17 V
0.13 V

VG,BENT

0050100
Time (ns)150

Fig. 5. Simulated VG waveforms with varied TJ at n = 3 and IL = 5 A.

SPICE simulation of the circuit. In the circuit simulations, n isvaried to emulate the bond wire lift-off, and TJ and the load
current (IL) are varied, because TJ and IL are key parameters todetermine VG during the operation of the power converters.
Fig. 4 shows the simulated VG waveforms with varied n at
TJ = 75 ºC and IL = 5 A. VG,MIN clearly changes with n, while
VG,BENT does not depend on n, because the RS and LS changewith n as shown in Table I and VG,BENT is determined by VTH.

No change

Gaussian connection

Full connection

Full connection

Sub-sampling (Max-pooling)

Convolution

VG,MIN

Convolution

Simulated

n=3
TJ = 75 ºC

5Layer 3:Layer 7:Layer 5:
Feature maps
Output120
16x264x1
6Layer 6:
Layer 2:
Layer 4:84
Feature maps
Feature map
6x288x1
16x132x1

Time

IL = 2 A
IL = 5 A
IL = 8 A

10VG (V)Input
Layer 1:500x1 Feature map
6x476x1

Sub-sampling (Max-pooling)

15VG

Fig. 9. CNN network adopted for measurement.

00

50100Time (ns)150

VG,MIN (V)Fig. 6. Simulated VG waveforms with varied IL at n = 3 and TJ = 75 ºC.

4.5

n=3

4.0

n=2

3.5Regsioresnen li(a)0-mWSimulated
TJ = 25 ºC - 125 ºC
IL = 5 A

n=1

2.6

2.7

VG,BENT (V)

2.8

Anomaly score

0.2

00

n=2III.

1020Time series data

3.33-mW5-mW

10-mW

When n increases, only VG,MIN decreases as shown in Fig. 4.
The regression line for n = 3 to calculate the anomaly score isalso shown. When the bond wire lift-off occurs, n changes
from 3 to 2, the distance between the data and the regression
line increases, and the anomaly score increases, thereby
detecting the bond wire lift-off. Fig. 8 shows the simulated
time series data of the anomaly score at IL = 5 A. In each data
point, TJ is randomly changed between 25 ºC and 125 ºC. n is
3 in the first 20 data points, while n is 2 in the last 10 data
points. When n changes from 3 to 2, the anomaly scoresuddenly increases from sub-0.04 to above-0.2, thereby
detecting the bond wire lift-off.

Bond wire lift-off
is detected.

n=32.5-mW(b)Fig. 10. (a) Measurement setup of IGBT. (b) Varied RE to emulate the bondwire lift-off.

Fig. 7. Simulated relationship between VG,MIN and VG,BENT with varied TJ andn at IL = 5 A.

0.12-mW30Fig. 8. Simulated time series data of anomaly score at IL = 5 A.

Fig. 5 shows the simulated VG waveforms with varied TJ at n
= 3 and IL = 5 A. Both VG,BENT and VG,MIN change with TJ,
because TJ changes VTH. Fig. 6 shows the simulated VG
waveforms with varied IL at n = 3 and TJ = 75 ºC. Both VG,BENTand VG,MIN do not depend on IL. The above results show that n
can be estimated by a regression analysis of VG,BENT and VG,MIN,
because VG,BENT depends on TJ, and VG,MIN depends on both nand TJ.
Fig. 7 shows the simulated relationship between VG,MINand VG,BENT with varied TJ and n at IL = 5 A. When TJ increases,
both VG,BENT and VG,MIN decrease, because VTH is reduced.

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ESTIMATION OF RE, TJ, IC, AND VTH USINGCONVOLUTIONAL NEURAL NETWORKIn order to detect the power device degradation, in thischapter, the estimation of RE, TJ, IC, and VTH using CNN withVG waveforms of an IGBT for input is shown using both
simulations and measurements [5]. In [5], the estimation of RE,TJ, IC, and VTH using simulations and the estimation of RE andIC using measurements are shown. In this paper, themeasurements are shown.
Fig. 9 shows the CNN network architecture adopted for ameasurement. The input is a voltage waveform of the gate
driver output during the turn-on of IGBT. The input vector
length is chosen to be 500, which corresponds to 6-ns cycle
sampling for the entire turn-on process of 3-µs. The output isthe categorized in 6 classes.
Fig. 10 shows the measurement setup with an IGBT
(2MBI100TA-060-50, 600 V, 100 A) and varied RE to emulate
the bond wire lift-off. RE is varied by changing the number ofresistors in parallel. The measured waveforms are shown inFig. 11. It is seen from the measured waveforms that it isREFERENCES
IC=2A

IC= 10A

IC= 20A

IC= 40A

IC= 50A

IC= 60A

IC= 30A

IC= 70A

VG (V)

15V

IC= 80A

IC= 90A

7.5V0V

IC= 100A

0

1.5µs

3µs

Fig. 11. Measured VG waveforms with varied IC.
Table II Six classes of RE and IC.
(a) RE: Emitter resistanceClass #012

3

4

5Label: RE=0mW

2mW

2.5mW

3.33mW

5mW

10mW# of waveforms

1100

1100

1100

1100

1100

1100

6600

5TotalTotal[1] V. Smet, F. Forest, J.-J. Huselstein, F. Richardeau, Z. Khatir, S. Lefebvreand Berkani, “Ageing and failure modes of IGBT modules in hightemperature power cycling,” IEEE Trans. Ind. Electron. vol. 58, no. 10,pp. 4931-4941, Oct. 2011.
[2] Z. Wang, B. Tian, W. Qiao and L. Qu, “Real-time aging monitoring for
IGBT modules using case temperature,” IEEE Trans. Ind. Electron. vol.
63, no. 2, pp. 1168-1178, Feb. 2016.
[3] X. Zeng, Z. Li, W. Gao, M. Ren, J. Zhang, Z. Li and B. Zhang. “A novel
virtual sensing with artificial neural network and k-means clustering for
IGBT current measuring,” IEEE Trans. Ind. Electron. vol. 65, no. 9, pp.
7343-7352, Sep. 2018.
[4] N. Baker, S. M. Nielsen, F. Iannuzzo and M. Liserre, “Online junction
temperature measurement using peak gate current,” IEEE Applied PowerElectronics Conference and Exposition, pp. 1270-1275, 2015.
[5] K. Miyazaki, Y. Lo, A. K. M. M. Islam, K. Hata, M. Takamiya, and T.
Sakurai, "CNN-based Approach for Estimating Degradation of Power
Devices by Gate Waveform Monitoring," IEEE International Conference
on IC Design and Technology, pp. 104-107, June 2019.

(b) IC: Collector current
Class #0

Label: IC range# of waveforms0A1215A
120030A6003445A
1200

60A
600

75A
1200

100A
18006600

Table III Estimation results using CNN.

Guessed class(a) RE: Emitter resistance012345Ratio

0275
000001Success rate: 0.995
Correct class12345Ratio
300
000.989265
000010264000
100262400.98500127900.9960000297
10.98910.996 0.986
1# of epochs: 74(b) IC: Collector current

Guessed class

Success rate: 1.000

012345Ratio

0315
00000
11014400001Correct class2300002720015500001145Ratio
001001001001298010466111# of epochs: 213

difficult to estimate RE and IC using human eyes. The machinerecognition is needed for estimating the parameters. RE and IC
are categorized into 6 classes this time as shown in Table II.
The results of estimation using the CNN approach aretabulated in Table III. The success rate is very high amounting
up to 99.5%. The number of epochs in the table shows thenumber of deep learning optimization loops needed for theentire learning process. Using i7 Intel processor with the clock
rate of 2.5-GHz, one epoch needs approximately 1-s, which is
sufficiently fast to be practical.
IV. CONCLUTIONS
Two power device degradation estimation methods by themachine learning of gate waveforms are proposed anddemonstrated with simulations and measurements.
Introducing the machine learning into the power electronics
will be a key to increase the reliability of the power devices
and to reduce the cost for various sensors.

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