IEEE ICICDT 2019, Suzhou, China

Session 7 - AI and Algorithm

CNN-based Approach for Estimating Degradation of Power Devices byGate Waveform Monitoring
Koutaro Miyazaki, Yang Lo, A. K. M. Mahfuzul Islam, Katsuhiro Hata, Makoto Takamiya, and Takayasu Sakurai
Institute of Industrial ScienceThe University of Tokyo
Tokyo, Japan{koutaro, yanglo, mahfuzul, khata, mtaka, tsakurai}@iis.u-tokyo.ac.jp
paper, the first successful application of a deep learning is
described to estimate reliability-related parameters only bymonitoring gate drive waveforms. As RE can be estimated ina live power system, it is possible to know when to replace
power devices before a system breakdown.

Abstract—A Convolutional Neural Network (CNN) is applied
to estimate the emitter resistance (RE), the junction
temperature (TJ), the collector current (IC), and the thresholdvoltage (VTH) of power devices just by monitoring the gate
drive waveforms. RE, TJ and IC are essential parameters for
power device reliability. By using the detailed circuit
simulation results for IGBT, it is shown that the abovementioned four parameters can be estimated with the successrate more than 93% by the proposed AI-based approach forthe first time. The measurement results also show that RE andIC are successfully estimated with the success rate >99%. Thediscussions are made on the success rate change depending on
the resolution and the sampling rate of an A/D converter andthe convolution filter kernel size.

II. TARGET SYSTEM AND CURCUITSConventionally, in order to know the values of RE, TJ,
VTH, and IC, it is customary to put sensors for current,
voltage and temperature in the main circuit as shown in Fig.1(a). Instead, in this paper, only gate driving voltage
waveforms are monitored to estimate these parameters asshown in Fig.1(b). This approach reduces the cost for various
sensors and related circuits, assembly and wiring. The gate
drive waveforms in power electronics circuits include lots of
information through the parasitic coupling capacitance, theparasitic inductances, and resistances within the loop of thegate drive and the emitter terminal of the power device.
Since the gate driver circuit is becoming digital andsmarter as described in [5], the gate driver chip can integrate
an A/D converter easily and in a cost-efficient way.
Moreover, it is not difficult to implement CNN-based
recognition system on the gate driver chip in the future.
There is no need to integrate the performance-hungry
machine learning system, but only the recognition logic
needs to be integrated. Even if the integration of the CNN on
the digital gate driver is not a choice, it is not difficult tosend digital data from the A/D converter to a main controller
and conduct the recognition process, since it can be slow
because only the long-term degradation is of interest here.

Keywords-CNN, Neural Network, AI, IGBT, power device,degradation, reliability, gate driver, IoT

I. INTRODUCTION
A long-term degradation of power devices induces severe
reliability issue of power systems and thus estimating thedegradation of power devices are vitally important. Amongpower devices, IGBTs (Insulated Gate Bipolar Transistor)
are most widely used for high-voltage, high-current
applications and the effects of the device breakdown aresignificant. For that reason, the IGBTs are mainly discussed
in this paper, although the other types of power devices, suchas a SiC MOSFET is also successfully handled with theproposed scheme.
The most frequent cause of the long-term degradation of
the IGBTs is considered to be the emitter resistance increase
due to the crack developing of an emitter contact, thepeeling-off of an emitter pad, the partial detachment of anemitter bonding and so on [1,2]. These degradations overtime are induced from heat cycles and the high current, thatis, RE, TJ and IC are essentially important parameters for
power device reliability.
The estimation of IC for an IGBT using the collectoremitter voltage, VCE, gate-emitter voltage, VGE, VTH, and TJ
as input has been investigated based on a neural network [3],it is very costly to know these voltages and TJ beforehand
using sensors and only IC can be estimated. The estimation ofTJ by measuring the peak voltage of the gate drive voltage
has been proposed [4] but the estimation is only for TJ andsince it does not use an AI-based approach, the estimationneeds very high precision of the peak voltage measurement
without noise, which is practically very difficult. In thisFig. 1. (a) Conventional and (b) proposed schemes for parameter estimation.
Blue parameters are input data and red parameters are calculated andestimated from input data.

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Paper 7.2

IEEE ICICDT 2019, Suzhou, China

voltage (VTH), The success rate is 0.98 for RE. Thus, it ispossible to suggest the increase in the emitter resistance
before it becomes alarmingly high.

III. CONVOLUTIONAL NEURAL NETWORK APPROACH
In this paper, the approach used for estimating theparameters is a widely-used deep learning approach, CNN.
Other types of machine recognition algorithms are also tried,
namely LDA (Linear Discriminant Analysis) and PCA(Principal Component Analysis) together with KNN (KNearest Neighbor classification). Both algorithms failed tocorrectly estimate parameters. Especially, estimating RE
turned out to be difficult with the low success rate of lessthan 50%.
The CNN machine learning software is implemented byPython and Keras [6]. The network architecture adopted fora measurement is shown in Fig.2. The input is a voltage
waveform of the gate driver output during the turn-on ofIGBT. The input vector length is chosen to be 500, whichcorresponds to 6-ns cycle sampling for the entire turn-on
process of 3-µs. The output is the categorized in 5 classes forthe simulation in Section IV and 6 classes for themeasurement in Section V. The way how the classes are
categorized are tabulated in the corresponding Section.

VCC

LVCC

LC
RG

RE

LGGND

LGND

Fig. 3. Circuit diagram used for simulation
Table I. Class number, labels and number of simulations for each class(a) RE: Emitter resistance

Gaussian connection

Full connection

Full connection

Sub-sampling (Max-pooling)

Convolution

Sub-sampling (Max-pooling)

TJ
VTHLE

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

Convolution

ICVG

VIN

Layer 1:
Input
500x1 Feature map
6x476x1

time

LG

Class #01

Label: RE range 0m

# of waveforms

22m

22000

4m

22000

346m

220008m

22000

Total
10m

22000

110000(b) TJ: Junction temperature
Class #0

Label: TJ range

Voltage# of waveforms

1240ºC

25ºC

22000

Fig. 2. Employed neural network layer architecture

55ºC
22000

3470ºC

22000

85ºC

22000

Total
100ºC

22000

1100004Total(c) IC: Collector current
Class #IV. RESULTS WITH SIMULATION DATA
Double pulse simulation is carried out using the circuit inFig.3 to generate training data and test data. Table I shows
the category of classes for each condition. Varying
parameters of the emitter resistance (RE), the junction
temperature (TJ), the collector current (IC), and the thresholdvoltage (VTH), 110000 waveforms are generated and used as
the machine learning input samples and the recognition testsamples. The ratio of the number of the leaning samples, thevalidation samples and the test samples is chosen to be
0.6:0.2:0.2.
First, the convolution filter size (kernel size) is optimized.
It is conducted by changing the filter size from 2x1 to 10x1.
The overall best success rates for four parameters are
obtained when the kernel size is chosen to be 8. Thus, thefollowing parameter estimation attempt is made with thisfilter size.
The parameter estimation results are tabulated in Table
III. Four neural networks are generated, each of which is aspecific CNN for the emitter resistance (RE), the junction
temperature (TJ), the collector current (IC), and the threshold

105

0

Label: IC range

1# of waveforms

220A

15A
22000

25A

22000

330A

22000

35A
22000

40A
22000

110000(d) VTH : Threshold voltage
Class #0

# of waveforms

12

3.6V

Label: VTH range 3V

22000

4.2V
22000

34

4.8V

22000

5.4V

22000

Total
6V

22000

110000In the label description, a class does not include the lower limit but includes
the upper limit.
Table II. Convolution filter size (kernel size) optimization
Parameter to beestimatedConvolution filter size
2345678910RE

Emitter
resistance

0.76 0.83 0.96 0.82 0.70 0.91 0.98

0.89 0.75

TJJunction
0.96 0.98 0.99 0.99 0.99 0.99 1.00
temperature

0.99 0.99

IC

Collector
current

1.00 1.00 1.00 1.00 1.00 1.00 1.00

1.00 1.00

VTH

Thresholdvoltage

1.00 1.00 1.00 1.00 1.00 1.00 1.00

1.00 1.00(Values are success rate.)

Best choiceIEEE ICICDT 2019, Suzhou, China

Session 7 - AI and Algorithm

estimating the parameters. RE and IC are categorized into 6classes this time as shown in Table IV.
The results of estimation using the CNN approach aretabulated in Table V. The success rate is very highamounting up to 99.5%. The number of epochs in the tableshows the number of deep learning optimization loops
needed for the entire learning process. Using i7 Intelprocessor with the clock rate of 2.5-GHz, one epoch needs
approximately 1-s, which is sufficiently fast to be practical.
In order to know the robustness of the estimation, one classof data (sample waveforms) is excluded from the learning
process. The results are shown in Table VI. The excluded
class of data when tested is classified to the nearest class formost of the cases. This means that the proposed estimator
based on the deep learning understands the ordering of RE
magnitude and thus can be considered as a naturally robust
estimator.
After all, it has been shown that the proposed method iseffective not only in the simulations but also in the realenvironments.

Table III. Achieved success rate for estimating four parameters

Guessed class(a) RE: Emitter resistance

Success rate: 0.983
Correct class
0123
4
Ratio
05430
470000.991
1110 5316
26000.975
20525426
3200.984
300705444
420.980
4000835422 0.985
Ratio 0.980 0.982 0.983 0.980 0.992# of epochs: 200

Guessed class(b) TJ: Junction temperature
0054031020
3040Ratio 1.00

Success rate: 1.00
Correct class
1234Ratio
0000
1.00
5657
0001.00
05491
001.0000550601.00
0005443 1.00
1.00 1.00 1.00 1.00
# of epochs: 200

Guessed class(c) IC: Collector Current
00557710203040Ratio 1.00

Success rate: 1.00
Correct class
1234Ratio
00001.00
5499
0001.0005478
001.0000545301.00
0005493 1.00
1.00 1.00 1.00 1.00

# of epochs:200(a) Measurement setup photograph

Guessed class(d) VTH: Threshold voltage
0055591020
30
40Ratio 1.000-m

Success rate: 1.00
Correct class
1234Ratio
00001.00
5568
0001.0005382
001.0000547501.00
0005516 1.00
1.00 1.00 1.00 1.00

2-m

2.5-m

3.33-m

5-m

10-m

(b) Emitter resistance is varied by changing resistor bank configurations
Fig. 4 Measurement setup: (a) overall view, (b) mechanism to show how tochange emitter resistance (RE)# of epochs: 200

V. RESULTS WITH MEASUREMENT DATA
The same estimation scheme is applied for themeasurement using IGBT whose model number is
2MBI100TA from Fuji electric. The measurement setup isshown in Fig.4 where the emitter resistance is varied bychanging the number of resistors in parallel. The measured
waveforms are shown in Fig.5. In the measurement, only theemitter resistance (RE) and the collector current (IC) arevaried, since it was difficult to change VTH and TJ in themeasurement setup this time. It is seen from the measured
waveforms that it is difficult to estimate RE and IC using
human eyes. The machine recognition is needed for

IC=2A

IC= 10A

IC= 20A

IC= 40A

IC= 50A

IC= 60A

IC= 30A

IC= 70A

VG (V)

15V

IC= 80A

IC= 90A

IC= 100A

7.5V0V0

1.5µs

3µsFig. 5 Measured waveforms. Each figure contains multiple of VG waveforms
corresponding to various RE conditions.

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Paper 7.2

IEEE ICICDT 2019, Suzhou, China

12345Total

Label: RE=0m

2m

2.5m

3.33m

5m

10m

# of waveforms

1100

1100

1100

1100

1100

1100

6600

(b) IC: Collector current
0

Label: IC range

0A# of waveforms

115A

1200

230A

600

3445A
1200

60A
600

575A

1200

Total
100A

1800

6600

Success rate

Class #Table V. Estimation results for measurement data(a) RE: Emitter resistance

Guessed class

Success rate: 0.995

012345Ratio

0275
000001

Guessed class0315
000001Class to which
excluded classdata is classified

Table VI. Estimation results when a class of data are excluded from learning
0m
0m
2m
2.5m
3.33m
5m
10m

1100
0000

Excluded class (n=1100)2m
2.5m 3.33m
434
00
701
0666
190
0398
01760
0
0150

5m
000815

10m
00053
1047

285

VI. DISCUSSIONS AND CONCLUTIONS

Fitting curve
0102030 40 50 60 70
Sampling interval (ns)8090100T J : Junction temperature

10.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.108 bit
10 bit12 bitFitting curve
102030 40 50 60 70
Sampling interval (ns)

80

90

100

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0VTH : Threshold voltage

8 bit
10 bit12 bitFitting curve
0102030 40 50 60 70
Sampling interval (ns)

80

90100

IC : Collector current

10.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.108 bit
10 bit12 bitFitting curve
0102030 40 50 60 70
Sampling interval (ns)8090100Fig. 6 Success rate depending on sampling rate and resolution of A/DACKNOWLEDGMENT
Useful discussions with Prof. Ichiro Omura and Prof.
Keiji Wada are acknowledged. This work is supported by theNew Energy and Industrial Technology Development
Organization (NEDO) of Japan.

(b) IC: Collector current

01
2345Ratio

8 bit
10 bit12 bit0Correct class12345Ratio
300000.989
265
000010264
000
100262400.98500127900.9960000297
10.98910.996 0.986
1# of epochs: 74
Success rate: 1.000
Correct class12345Ratio
000001144000010272000100155001000298010000466
111111# of epochs: 213

RE : Emitter resistance

8bit, 6ns

Success rate

01
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Success rate

Class #Success rate

Table IV. Number of measurements for each class(a) RE: Emitter resistance

REFERENCES[1] V. Smet, F. Forest, J.-J. Huselstein, F. Richardeau, Z. Khatir, S.
Lefebvre and Berkani, “Ageing and failure modes of IGBT modules inhigh-temperature 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. “Anovel virtual sensing with artificial neural network and k-meansclustering 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 AppliedPower Electronics Conference and Exposition (APEC) 2015 IEEE, pp.
1270-1275, 2015.
[5] K. Miyazaki, S. Abe, M. Tsukuda, I. Omura, K. Wada, M. Takamiyaand T. Sakurai, "General-Purpose Clocked Gate Driver (CGD) IC withProgrammable 63-Level Drivability to Reduce Ic Overshoot andSwitching Loss of Various Power," Applied Power Electronics
Conferenceand Exposition (APEC), pp. 1640- 1645, 2016.
[6] Keras: The Python Deep Learning library: https://keras.io/

The discussions are made on the success rate dependence
on the resolution and the sampling rate of the A/D converter.
The success rate depends of these parameters as shown inFig.6. 6-ns sampling and 8-bit resolution are acceptable toobtain the reasonably high success rate for estimation. AnA/D converter with 6-ns sampling and 8-bit resolution iseasy to implement on a Si chip. Thus, it can be said that thisapproach is practical and provides promising way to estimate
long-term degradation of power devices.

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