Automatic Optimization of IGBT Gate Driving
Waveform Using Simulated Annealing for
Programmable Gate Driver IC
Koutarou Miyazaki, Makoto Takamiya and Takayasu Sakurai
University of Tokyo
Tokyo, Japankoutaro@iis.u-tokyo.ac.jp

IC
VCE
VGE

63 NMOSs

optimization;

VGE

Turn-on of IGBT
n∞=63
n1
n4
n
2
n3
n0=0
t

nNMOS

(a)

I. INTRODUCTION
Gate driving waveform optimization of power devices for
reducing energy loss without inducing large current or voltage
overshoot has been attracting attention [1-9]. Recently for thispurpose, a programmable gate driver IC has been developed[4], which enables to generate almost arbitrary waveforms forthe gate voltage by changing the number of driving
NMOSFETs and PMOSFETs at a certain time step. It can drive
various power devices such as power MOSFETs, Si IGBTs and
SiC MOSFETs. The optimization of the gate waveform,
however, has to be conducted manually by human because of
inaccurate IGBT simulation model and the parasitic
components.Thus finding satisfactory waveforms takes days oftime and the search space is very limited. Although the
optimization strategies were presented in previous publications,the exact optimization depends on the board design, thesystems design and the component parts, and thus
quantitatively identifying the optimized waveform for a
specific case still needs much effort. Moreover, as the freedomof the gate waveform control goes up, the optimization process
is getting increasingly difficult. This paper reports an automatic
optimization of the gate waveform by dynamically combining
real measurements and software optimization loop to cope with
the inherently time-consuming and costly optimization process.

IL(=52A)

VDD=15V 63 PMOSs

SiC diode

Programmablegate driverTurn-off of IGBTn1 n n3
n4 n∞=63
2
n0=0
t

VGE

Keywords—IGBT; Gate driver; Automatic
Simulated annealing; insert (key words)

VDC=500V

Control signals:
nPMOS and nNMOS

Abstract—Optimizing the gate driving waveform of powerdevices for energy loss and noise per switching has been
attracting attention. This paper proposes a systematic method toautomatically optimize the gate waveform by dynamicallycombining real measurements and software optimization loop
based on simulated annealing algorithm. The method is applied
to the turn-on and turn-off process of an IGBT double pulse test
setup. A gate driving waveform with four time segments is
employed for the optimization, which is realized by a help of aprogrammable gate driver IC. The machine-based optimization
finishes within one and half hours and with the resultant
optimized waveform, 59% energy loss decrease and 57% voltage
overshoot reduction are achieved for the case of the IGBT turnoff compared with a simple single-step gate driving waveform.

IOVERSHOOT
IC

IL

t
E LOSS =  IC VCE dt
VCE

VCE

TSTEP
4 time-segmentstt
VOVERSHOOT
IC

TSTEP
4 time-segmentst(b)Fig. 1 (a) Circuit diagram and (b) waveform sketches used for
IGBT gate driving waveform optimization

Measured
waveform data

Temperature T=T 0；
Set waveform vector X=X0； /*X=[n1, n2, n3, n4]*/
While (fOBJECT shows any gain) {Oscilloscope (TDS3054C)
Current & voltage signals

LabVIEW(PXI6552)

Simulated annealing algorithm(MATLAB on PC)

Generate new waveform vector XTRIAL randomly;
nNMOS
Device under test (DUT)/*XTRIAL=[n1, n2, n3, n4]*/or
XTRIAL∆fOBJECT = fOBJECT(XTRIAL) - fOBJECT(X)；nPMOS
if (∆fOBJECT <0) then X=XTRIAL；
else
Output
X=XTRIAL with probability of exp(-∆fOBJECT/T) ； nNMOS or nPMOS witha certain time-stepend ifusing waveformT = r * T； (ex. r=0.998)}
Programmable
IGBT circuit forvector XTRIAL.
gate driver IC
double pulse test in Fig.1

Fig. 2 System setup for automatic optimization (yellow part is hardware and blue part is software)

II. OPTIMIZATION METHODThis paper proposes the automation of the gate waveform
optimization by using the simulated annealing (SA) algorithm[11] and a programmable gate driver IC [10]. The system setup
used in this paper is shown in Fig.1. A 500-V double pulse test
circuit is built using Si IGBT (IRG7PH46UPbF) and SiC diode(C4D10120D) with the load current of 52-A, which is driven+15V/0V gate voltage levels by a programmable driver IC with
64-level current resolution and 40-ns minimum time step [10].
The actual time step, TSTEP, used for the IGBT turn-on casewhose time constant is mainly determined by the SiC diode is
80-ns and that for the IGBT turn-off case is 400-ns, which isdetermined by the Si IGBT itself. For both cases, the numberof the time-segments is set to 4 in this paper as shown in Fig.1.
After these segments, all PMOS or NMOS are on-state in order
to avoid malfunction by reducing the gate impedance.
The programmable gate driver IC accepts a sequence ofnPMOS (the number of on-state PMOSs) and a sequence ofnNMOS (the number of on-state NMOSs) as the control inputs
and changes the output drivability accordingly. Any strength ofdrivability from 0 to 63 (x 12-mA for both of PMOS andNMOS) can be chosen for each of the 4 time-segments, that is,n1, n2, n3, and n4 in Fig.1 can be any of integers from 0 to 63.
Here, a set of the 4 integers [n1, n2, n3, n4] is called a waveform
vector. Since the driver NMOS and PMOS are operated in asaturation region, an almost constant-current driving condition
holds in driving the IGBT. Details of the gate driver IC are
disclosed in [10].
In this waveform optimization process, 644 (~1.7e7)
different waveforms need to be tried for an exhaustive search,which is impractical. The machine-based optimization using
the simulated annealing (SA) is proposed to make theoptimization practical. The objective is to reduce the energy
loss, ELOSS (=  IC VCE dt ), and at the same time to mitigate theovershoot of IC, IOVERSHOOT, for the turn-on case (the overshootof VCE, VOVERSHOOT, for the turn-off case), whose definitions

are shown graphically in Fig. 1. In order to achieve this goal,
first, the energy loss and the overshoot values are normalized
as below. The normalization helps to balance the energy loss
optimization and the overshoot optimization.
E 'LOSS =E LOSS − E LOSS,MIN
E LOSS,MAX − E LOSS,MIN

I 'OVERSHOOT =,IOVERSHOOT − IOVERSHOOT,MIN
IOVERSHOOT,MAX − IOVERSHOOT,MIN

V 'OVERSHOOT =,VOVERSHOOT − VOVERSHOOT,MIN
VOVERSHOOT,MAX − VOVERSHOOT,MIN,

where ' signifies the normalized quantity and the subscript MIN(MAX) signifies the minimum (maximum) of thecorresponding quantity. For example, ELOSS,MIN is the measured
ELOSS when the gate driving waveform is the fastest, that is, allof n1, n2, n3, n4, and n∞ are 63. On the other hand, ELOSS,MIN is
the measured ELOSS when the gate drive waveform is theslowest, that is, all of n1, n2, n3, n4, and n∞ are 1. Likewise, theother minimum (maximum) overshoot values are obtained
either by the slowest or the fastest gate driving waveform.
After the normalization, all of the normalized quantities vary
between 0 and 1. The object function, fOBJECT, to be minimized
is set as the Euclidean norm as below.

f OBJECT = E 'LOSS 2 + I 'OVERSHOOT 2

for the IGBT turn-on case.
f OBJECT = E 'LOSS 2 + V 'OVERSHOOT 2

for the IGBT turn-off case.
The total system setup is shown in Fig. 2. A pseudo-codeof the SA algorithm is shown in the figure. The algorithm isimplemented on a PC using MATLAB and generates a newtrial waveform vector, XTRIAL=[n1, n2, n3, n4]. Using thisXTRIAL vector as an input, LabVIEW generates control signals
to the programmable gate driver. Then the device under test
feeds back the measured voltage and current waveform data toELOSS (mJ) IOVERSHOOT (A)fOBJECT

6
4
2
0
10
5
0
1

0.5
0
0

500

1000
1500
Optimization iteration count in SA

2000

2500

Fig. 3 History plots of IOVERSHOOT, ELOSS and fOBJECT during optimization process for turn-on(b) Waveforms at optimization end point(a) Waveforms at optimization start point
VCE : 100V/div
IC : 10A/div
VGE : 5V/div

VCE : 100V/div
IC : 10A/div
VGE : 5V/div

0

0
100
63

50
0

63

63

63

63

nPMOS

nPMOS

100
Initial gate drive waveform vector
0
0

80ns
0.2

Time (μs)

0.4

0.6

Optimized gate drive waveform vector

50
0

32
0
0

40
16
0.2

63

20
Time (μs)

0.4

0.6

Fig. 4 Measured waveforms at (a) start point and (b) end point of SA optimization for IGBT turn-on case
the PC through an oscilloscope. Using the measured waveform
data, PC calculates the object function, fOBJECT based on theabove-mentioned expressions. Depending on fOBJECT, the PC
generates the next trial waveform, XTRIAL, according to the SA
algorithm. The optimization itereation loop continues until nodecrease in fOBJECT is observed for a certain period. Other
greedy algorithms were tried but stuck at sub-optimal points
while the SA always finds much better optimal points.

III. RESULTS FOR TURN-ON OF IGBTThe automatic optimization setup is applied to the turn-on
case of the IGBT. In this case, a waveform vector [n1, n2, n3,n4] signifies the number of on-state driver PMOSs, nPMOS, at agiven time-segment. The initial waveform vector is set as [63,63, 63, 63], that is, the fastest single-step turn-on case. Figure 3
shows the history plots of IOVERSHOOT, ELOSS and fOBJECT during
the optimization process. The optimization process stops after

2420 iterations in this case. One iteration takes 2.2 seconds
including the measurement time and the data transfer time toand from the PC. Thus, the total optimization takes 5324-s
which is about one and a half hour. Multiple of the
optimization processes were tried and all the trials finishedwithin two hours.
The object function, fOBJECT, is successfully minimized
from 0.56 to 0.12 in this case. Just for information, a simple
greedy optimization method where each of n1, n2, n3, and n4 ischanged by either -1, 0 or 1 and the waveform vector which
lowers fOBJECT is searched, the optimization process is stuck at
the initial point and never gives any better point. Thus, theoptimization methods that can have the ability to get out fromthe local minima such as the SA is needed. No destructive
breakdown of power devices was observed in the optimization
process. The proposed method is demonstrated to be effective
for an IGBT gate waveform optimization.

nPMOS8

0.2
0.4Time (μs)
37

nPMOS

0

0

36

0.6

32 16 20 40
0.2
0.4Time (μs)

fOBJECT= 0.7
0.6

5
0.4

3

0.6

0.2

0

-40%

0.1
0.6

0.8

1.2

1

0.9

0.6

0.4

0.3

2

63

1.1

0.5

4

61 63

18
0.2
0.4
Time (μs)

0016 19 23

1.3

0.6

0.8

1

0

Single-step drive
18
0.2
0.4
Time (μs)

22.5IOVERSHOOT (A)Waveforms in
Fig.4(a) (waveforms
at optimization startpoint) correspond to
this gate driving
waveform vector,
which is the starting
point of SA.

0.2

0.6

-36%
1.5

1

63

nPMOS

35

6

nPMOS

nPMOS

0

0

63

1
0.2
0.4
Time (μs)

007ELOSS (mJ)nPMOSThree examples of
optimized
waveform vectors

Single-step drive1Normalized ELOSS (=E’LOSS)

Normalized IOVERSHOOT (=I’OVERSHOOT)
0.2
0.4
0.6
0.8

0

0

3

0

0

Single-step drive
0.2
0.4
0.6
Time (μs)

Fig. 5 ELOSS - IOVERSHOOT trade-off. Broken lines are equi-fOBJECT contour. Red triangles are optimized point.

(a) Waveforms at optimization start point(b) Waveforms at optimization end point

VCE : 100V/div
IC : 10A/div
VGE : 5V/div

VCE : 100V/div
IC : 10A/div
VGE : 5V/div

0

0
63

50
0
0

63

63

63

63

Initial gate drive waveform vector
0

100

nNMOS

nNMOS

100

400ns
1
Time (μs)

2

Optimized gate drive waveform vector

50
0
0

34
0

1

4

63

25

1
Time (μs)

2Fig. 6 Measured waveforms at (a) start point and (b) end point of SA optimization for IGBT turn-off caseFig. 4 shows the measured waveforms at the initial point
and the end point of the optimization iterations shown in Fig. 3.
At the start, the waveform vector is a simple single-stepfunction and shows the higher IC overshoot. The improved gate
driving waveform turns out to drive relatively strongly at first
and then, reduce the drivability just before VGE reaches thethreshold voltage of the IGBT, and gradually increase thedrivability afterwards. This qualitative description of the
optimized waveform is consistent with the previous
publications [1,2,3] but the advantage of the proposed method
is to provide the quantitative values of parameters.
More than ten optimization trials using SA are carried out
and all the trials successfully found the optimized waveform
vector, that is, the optimized gate driving waveform. Three

examples of optimized waveform vectors are shown in Fig. 5.
ELOSS and IOVERSHOOT are in a trade-off situation. When ELOSS is
low with a fast gate drive, the current overshoot is large butwhen the current overshoot is decreased by the slower gate
drive, the energy loss increases. Thus, in Fig. 5, the trade-off isalso shown. With a simple single-step gate drive, the trade-off
is confined on the blue line even the drivability step height ischanged. With the more sophisticated gate drive using the
programmable gate driver IC, the achievable trade-off space isenlarged and 40% energy loss reduction and 36% current
overshoot reduction are attained compared with the single-stepgate drive.

12

10

2

1

5

1
Time (μs)

nNMOS

2
0.9

1

1.1

1.2

0.8

0.7

6

0.4

0.6
0
0

-59%

Single-step drive
61Time (μs)

2

0.4

0.3
0.2

0.2
63

35

0.6
0.5

8

8

1
Time (μs)

1Time (μs)
fOBJECT= 0.8

2
63

1

0
0

0

5

1
Time (μs)
35

0

6

12

1

25

1

1.3

nNMOS

1

0
0

0
0

Single-step drive

nNMOS

63

35

nNMOS

nNMOS

nNMOS

Three examples of
optimized
waveform vectors

ELOSS (mJ)

14

1

4

Normalized ELOSS (=E’LOSS)

Normalized VOVERSHOOT (=V’OVERSHOOT)
0.2
0.4
0.6
0.8

0

0
0

-57%

0.1

2
60

80

100
120
VOVERSHOOT (V)

140

160

0

Waveforms in
Fig.6(a) (waveforms
at optimization startpoint) correspond to
this gate driving
waveform vector,
which is the starting
point of SA.
63
Single-step drive
1Time (μs)

2Fig. 7 ELOSS - VOVERSHOOT trade-off. Broken lines are equi-fOBJECT contour. Red triangles are optimized point.
IV. RESULTS FOR TURN-OFF OF IGBT

V. SUMMARY

As for the IGBT turn-off case, the optimization procedure
is basically the same as the turn-on case. In this case, though, awaveform vector [n1, n2, n3, n4] signifies the number of on-statedriver NMOSs, nNMOS, at a given time-segment. Fig. 6 shows
the measured waveforms at the initial point and the end
point of the optimization process. The optimized gate driving
waveform turns out to drive relatively strongly at first and then,reduce the drivability just before VGE reaches the thresholdvoltage of the IGBT, and gradually increase the drivability
afterwards. This reduces the sharp voltage overshoot. The
qualitative strategy for the optimized waveform is consistent
with the previous publications [1,3] but the advantage of the
proposed method is to be able to find the quantitative values ofparameters in a practical time.

A novel search method based on simulated annealing
algorithm is described to find the better gate driving
waveforms for power devices. The method is shown to beeffective in optimizing the trade-off between the energy loss
and the waveform overshoot by using a double pulse test
configuration as an example. In this paper, the number of thetime-segments is set to four but it may be increased to 16 ormore for further better driving waveforms. Then, the search
space increases exponentially and the proposed automatic
optimization approach becomes more indispensable. Althoughan optimization example is depicted in this paper, the presented
SA-based optimization for the gate driving waveforms can beapplicable to the wider range of problems toward the better
circuit performance.

Three examples of optimized waveform vectors are shownin Fig. 7. It is seen that the three points are not exactly the samein the waveform vectors because of the stochastic nature of the
SA optimization process. Still, all the optimized waveform
shows considerable reduction in the object function and all
have the characteristics that the drivability is lowered in the
middle of the driving process. In Fig. 7, the trade-off between
the energy loss, ELOSS, and the voltage overshoot, VOVERSHOOT,
is also shown. With a simple single-step gate drive, the tradeoff is confined on the blue line even the drivability step heightis changed. With the more sophisticated gate drive using the
programmable gate driver IC and four time-segmented drive,the achievable trade-off space is enlarged and 59% energy loss
decrease and 57% voltage overshoot reduction are achieved
compared with the single-step gate drive.

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