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1. Application of Design of Experiments and Evolutionary Algorithms to
Self-Structuring Electromagnetic Scatterer and
Optimization of Antenna Structures
Dissertation Defense, June 4, 2012
Yen-Sheng Chen
National Taiwan University, Taiwan

2. Agenda
Motivation
Introduction
Contribution of this dissertation
RFID Antenna Conventional limitations
Application A novel tag structure and its validity
RCS Control Adaptive RCS control
Application Self-structuring electromagnetic scatterer
Design Tool An automatic antenna design tool
Application Wide- and multi-band antenna designs
2 of 54

3. Motivation
Why EM problems need optimization techniques?
The intelligence of optimization
methods helps engineers develop
sophisticated and powerful
applications!
The procedure terminates at a optimum
solution, instead of an acceptable one
It is a systematic procedure and gives unambiguous
instructions to solve problems
3 of 54

4. Contribution of This Dissertation
Conventional Intelligence of
Our idea optimization method
limitations
A dual-antenna
We propose a new tag
structure for Conventional structures DOE systematically
structure to have maximum
RFID tags are not optimized for both handles multiple
reception and maximum
reception and detection design considerations
differential RCS
Self-structuring
electromagnetic We lack a smart and We propose SSES for
FFD efficiently solves
scatterer (SSES) reconfigurable reflective RCS-reduction and
surface for RCS control reflectarray applications synthesis problems
An automatic
antenna design We develop a pixelized Evolutionary
The procedure of antenna
tool
designs is often tedious
design tool for practical algorithms act as the
design situations kernel of this tool
4 of 54

5. Agenda
Motivation
Introduction
Contribution of this dissertation
RFID Antenna Conventional limitations
Application A novel tag structure and its validity
RCS Control Adaptive RCS control
Application Self-structuring electromagnetic scatterer
Design Tool An automatic antenna design tool
Application Wide- and multi-band antenna designs
5 of 54

6. Limitations of Conventional Passive RFID Systems
When ZA=ZC*, maximal power
Reader Tag
transfer to the digital core
ZA
Rectifier Digital
ZC Core
Received
Signal State 2=Short
Backscatter Modulator
ZL=0 and ZL=ZC
State 1=Match Match/short introduce a smaller level difference in
Time the backscattered signals
K. Finkenzeller, RFID Handbook: Radio-Frequency Identification Fundamentals and Applications, 2nd ed.: Wiley, 2004. 6 of 54

7. Proposed Dual­Antenna Structure for Passive Tags
When Zre=ZC*, maximal power
Reader Tag
continuously supply to the chip
Receiving antenna
Zre
Rectifier Digital
ZC Core
Received
Backscattering antenna
Signal State 2=Short Zsc
Backscatter Modulator
ZL=0 and ZL=∞
State 1=Open When Xsc = 0, open/short introduce a
Time
larger level difference
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8. Further Details
The proposed dual-antenna tag
The tag IC with multiple RF ports has
been commercially used
The open/short impedance state can
be realized by a switching transistor
Each of the antenna has its design
considerations, and the mutual
coupling should be kept small
The co-design of the receiving and backscattering antennas
within a very small area is the most challenging task!
P. V. Nikitin and K. V. S. Rao, “Performance of RFID tags with multiple RF ports,” in Proc. IEEE-APS Symp., Honolulu, HI, June 2007,
pp. 5459–5462.
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9. How to Design Such a Complex Antenna Structure?
Receiving Backscattering Co-design of
antenna antenna the structure
For the continuous For the maximum The performance of
and maximum level difference of the antennas should
power reception backscattering signal be uncorrelated
Zre = Zc* Xsc = 0 Minimize |S21|
If we design the antenna structure with trial-and-error approaches...
The design process may fail because there are too many design goals
There is no guarantee that the best solution has been found
We need a systematic design method to study this problem!
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10. Our Strategy: Design of Experiments (DOE)
Benchmark structure
Frequencies: 902–928 MHz
8 decision variables
4 objective functions
Choose Zc = 33 – j 112 Ω
Meander dipole within a
small area: Rin ≈ 10 Ω
Response surface
Evolutionary algorithms Design of experiments
Black-box approach Uncover the black box
Search the solution space Treatment combination Build the solution sub-space
Every decision variable is Differentiate the significance
treated as equally important between decision variables
Less human bias More human interpretation
Solution space Random initialization
Blind search Solution space Designed treatment
R. A. Fisher, “The arrangement of field experiments,” Journal of the Ministry of Agriculture of Great Britain, vol. 33, pp. 503–513, 1926. 10 of 54

11. Step 1: Determine the Interested Sub­Region
Parameter Low (-1) High (+1)
w1 3.5 4
d1 0.8 1.2
t1 3 3.5
w2 2.5 3
d2 0.8 1.2
t2 2.75 3.25
How to decide the level of each factor? Set l1 = l2 = 7 mm
Design frequency: 915 MHz
Prior knowledge
Combining our EM knowledge and experience
Size limitations
The 32.8 × 32.8 mm2 area adds constraints to the choice of levels
Iterative strategy
As we learn more about which factors are important and which levels produce the best result,
the region of interest will usually become narrower
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12. Step 2: Allocate Suitable Treatment Combinations
Full factorial design Fractional factorial design (FFD)
`
Performing only a subset of 2k
The treatment combinations
combinations; it gains similar
are all the 2k enumeration
results but loss some accuracy
t1 t1
Example: (-,-,+) (-,+,+) Example: (-,+,+)
(+,-,+)
23 full design (+,-,+) (+,+,+) 23–1 FFD
(-,-,-) (-,+,-)
d1 (-,-,-) d1
(+,-,-)
w1 (+,+,-) w1 (+,+,-)
26 full design 26–1 FFD (resolution VI)
Performing 64 simulations, and it Performing designed 32 simulations
gives us the most detailed information
26–2 FFD (resolution IV)
Performing designed 16 simulations
Whatever experimental design it is, the factors are varied together,
instead of “one-factor-at-a-time”
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13. Step 3: Analyze Experimental Results
Main effect Two-factor interaction Higher-order interaction
The variation of Rre caused The variation of the main effect of The three-factor interaction between
by one single factor t2 toward Xre caused by d2 w2, t2, and d2 =
(The two-factor interaction of t2 and d2
w2– = 15.37 t2+ = 180
when w2 is at the high level) – (that of
t2– = 150 t2 and d2 when w2 is at the low level )
d2–
w2+ = 15.27
t2+ = 142
t2– = 132
d2+
Sparsity-of-effects principle
Two-factor interaction = Higher-order interactions are often
Main effect of w2 = w2 + – w2 = –0.1
–
very insignificant
(142–132)/2 – (180–150)/2 = –9.5
These effect estimates should be justified by formal statistical inferences!
They are realizations sampled from each effect’s distribution
Put insignificant effects in the models will waste resources when trying to optimize unimportant
factors
D. C. Montgomery, Design and Analysis of Experiments, New York: Wiley, 2005. 13 of 54

14. Step 4: Formulate Response Surface Models
It is convenient to cast the significant effects into response surface models!
k k −1 k k − 2 k −1 k
y = β 0 + ∑ β i xi + ∑
ˆ ∑β x x j +∑
ij i ∑ ∑β x x j x j + ... + β ij ...k xi x j ...xk
ijl i
where βi = Ei /2
i =1 i =1 j = i +1 i =1 j = i +1 l = j +1
For example, ⎛ w − 3.75 ⎞ ⎛ t − 3.25 ⎞ ⎛ w − 3.75 ⎞⎛ t1 − 3.25 ⎞
Rre ( Ω ) = 15.30 + 1.24 ⎜ 1
ˆ
⎟ + 4.75 ⎜ 1 ⎟ + 0.72 ⎜ 1 ⎟⎜ ⎟
⎝ 0.25 ⎠ ⎝ 0.25 ⎠ ⎝ 0.25 ⎠⎝ 0.25 ⎠
Rre Xre Xsc |S21|
Estimates Full R6-FFD R4-FFD Estimates Full R6-FFD R4-FFD Estimates Full R6-FFD R4-FFD Estimates Full R6-FFD R4-FFD
I0 15.32 15.30 15.23 I0 151.4 150.94 150.3 I0 -3.56 -4.51 -4.5 I0 -37.39 -37.07 -39.1
w1 1.22 1.24 0.98 w1 24.4 24.72 20.18 d1 -12.3 -12.95 -12.95 d1 -1.61 -2.86
t1 4.79 4.75 4.67 d1 7.16 t1 6.18 6.57 5.44 t1 -5.69 -5.29 -8
d2 -0.33 -0.35 t1 106.5 105.49 105.3 d2 7.17 6.79 6.53 t2 1.87 2.31
w1*t1 0.71 0.72 0.45 d2 -14.32 -13.14 -14.02 t2 71.48 71.71 71.52 w2 -2.93 -2.82 -3.23
d1*t2 -0.24 t2 9.76 w2 16.36 15.33 15.64 d1*t2 1.33
w1*t1 7.46 d1*t1 -2.98 t1*t2 -6.59 -6.26 -8.22
d1*t2 -5.24 w2*t2 3.3 t1*w2 1.29
t1*t2 3.48 t2*w2 -2.03 -1.8 -3.11
d2*t2 -4.73 t1*t2*w2 -3.83 -3.74 -4.49
t1*d2*t2*w2 1.41
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15. Step 5: Simultaneously Optimize the Four Objectives
We obtain 4 response Model the equality into Solve the non-linear Rank these solutions
surface models a constrained problem programming problem by Derringer’s
Min. |S21| s.t. by Matlab desirability functions
Rre = 33, Xre = 112,
102 ≤ Xre ≤ 122, A series of solutions Overall desirability
Xsc = 0, Min. |S21|
−10 ≤ Xsc ≤ 10, are found D = (d1d2d3d4)1/4
Rre ≥ 12
Number w1 d1 t1 w2 d2 Rre
t2 Xre Xsc |S21| D
1 0.97 0.48 –0.43 1 –0.39 0.86 17.36 112 –1.04 –38.86 0.77
As large as possible Hit the target As small as possible
d1 d2 d3 d4
1 1 1 1
0.67 0.90 0.59
0 Rre 0 Xre 0 Xsc 0 |S21|
12 17.36 20 102 112 122 –10–1.04 10 –38.86–30
–45
17.36 − 12 112 − 102 −1.04 − ( −10 ) −38.86 − ( −30 )
d1 = d2 = d3 = d4 =
20 − 12 112 − 102 0 − ( −10 ) −45 − ( −30 )
= 0.67 =1 = 0.90 = 0.59
G. Derringer and R. Suich, “Simultaneous optimization of several response variables,” Journal of Quality Technology, vol. 12, no. 4, pp. 214–219,
Oct. 1980.
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16. Verification 1: Isolation and Antenna Impedances
Zre under short state Zre under open state
|S21| = –46.1 dB @ 915 MHz
Simu. Meas. Performance
12.73 + 14.28 +
Open j113.09 j116.58 The impedance of the receiving
12.76 + 14.81 + antenna remains unchanged
Short j114.11 j107.29
DOE significantly optimizes the
isolation and achieve the design
goals in a systematic manner
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17. Verification 2: Receiving Performance
Experimental setup Experimental results
The receiving capability of the receiving antenna is stable!
The variation of receiving power is less than 0.2 dB
In contrast, the receiving capability of the conventional tag antenna severely degrades during
the short-circuited state
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18. Verification 3: Backscattering Performance
Examination of scalar differential RCS (ASK): Tag antenna
The scalar differential RCS of the dual-antenna structure is Tx antenna
much larger than the conventional tag design
Pr ( 4π ) d
3 4
Rx antenna
The reliability is thus improved σ=
Pt Gt Gr λ 2 d = 0.75 m
Conventional tag structure The proposed dual-antenna tag
Open / short
Max. RCS = –23.5 dB
Min. RCS = –31.9 dB Match
Receiving Backscattering
antenna antenna
Max. RCS = –24.4 dB
Min. RCS = –50.1 dB
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19. Verification 4: Enhancement of Detection Range
Examination of vector differential RCS:
Δ1
>1
If a coherent detection method is used by the readers, Δ2
the detection capability is proportional to:
Δ = Et ( Z L1 ) − Et ( Z L 2 ) = Γ ( Z L1 ) − Γ ( Z L 2 ) I m Er
The proposed tag structure have better detection since
that the impedance states are open and short
Associated detection range:
The backward detection range is determined by:
1
⎛ PG 2 λ 2 ⎞ 4
d max =⎜ t t
Δσ ⎟ EIRP = 4 W
⎜ ( 4π )3 S ⎟
⎝ R ⎠ Sensitivity = –80 dBm
The associated detection range remains unchanged
even if the chip impedance varies with the absorbed
power or operation frequency
R. B. Green, “The general theory of antenna scattering,” Ph.D. dissertation, Dept. Elect. Comput. Eng., Ohio State Univ, Columbus, OH, 1963. 19 of 54

20. Agenda
Motivation
Introduction
Contribution of this dissertation
RFID Antenna Conventional limitations
Application A novel tag structure and its validity
RCS Control Adaptive RCS control
Application Self-structuring electromagnetic scatterer
Design Tool An automatic antenna design tool
Application Wide- and multi-band antenna designs
20 of 54

21. Motivation 1: Adaptive RCS Control
RCS Reduction RCS Enhancement
Shaping, coating, and Phase shifters, varactors,
cancellation have been used and switches are used as
as RCS-reduction methods RCS-enhancement methods
Application: Absorber and Application: Navigation and
radar application reflective surface
Controlling RCS properties is so important, but we lack a smart
and reconfigurable surface to accommodate both the needs!
21 of 54

22. Motivation 2: Self­Structuring Devices
Self-structuring Self-structuring Reconfigurable
antennas (SSA), two-port network, electromagnetic
2000 2009 shutter, 2011
By opening and closing the By opening and closing the By opening and closing the
switches, SSA automatically switches, the device can switches, the device can
configures itself into acts as filter, attenuator, acts as an open or a closed
different missions phase shifter, and matching surface
The template extends to network, respectively
patch antennas in 2009
C. M. Coleman, E. J. Rothwell, J. E. Ross, and L. L. Nagy, “Self-structuring antennas,” IEEE Antennas Propagat. Mag., vol. 44, no. 3, pp. 11–23,
June 2002.
22 of 54

23. Our Idea: Self­Structuring Electromagnetic Scatterer
Self-Structuring electromagnetic scatterer (SSES)
Receiver-type Definition: A reflective surface which
z
sensor can adapt itself to new operational
θin objectives, such as RCS reduction
and RCS enhancement
SSES θ opt
template By opening and closing the switches,
various scattering properties are
Microprocessor produced, and the best configuration
…
x
is found by some efficient algorithms
N control lines
Its potential uses:
Bistatic absorber Space-wave phase shifter
Reflectarray application Space-wave attenuator
Reflector of antenna application Smart antennas
23 of 54

24. SSES Template
This template grounds on the scattering
properties of thin strips
Different strip lengths provide extra
If the strip length is identical, this
But if wedirection in certain direction this is notdirection
TheThe plane is not in phase Consider direction is in phase
interest is in phase direction this in phase
phases, so
Point
L1 L2 L3 L4 L5 L6 w d source Metal plate SSES
10 50 20 20 20 20 10 20
(Unit: mm; operational frequency: 1.5 GHz)
K. Barkeshli and J. L. Volakis, “Electromagnetic scattering from thin strips–Part I: Analytical solution for wide and narrow strips,” IEEE Trans.
Educ., vol. 47, pp. 100–106, Feb. 2004.
24 of 54

25. How to Find a Suitable State for Switches?
Different strip lengths are provided by opening/closing the switches,
and the best state of the switches is determined by binary algorithms
Conventional method: GA 1 0 0 0 1 1 … 0 1
GA doesn’t know the problem nature, simply
s1 s2 s3 s4 s5 s6 … s29 s30
performing a blind search
Objective function: σ(θin, θopt)
All the 30 switches have equal chance to
share the genetic operators
To find the most suitable state of the switches, Initial Fitness Selection
it takes 6000 functional evaluations population evaluation (s = 2)
(Npop = 120) (Measurement)
Elitist
Mutation Crossover
replace-
(pm = 0.1) (pc = 0.5)
ment
C. M. Coleman, E. J. Rothwell, and J. E. Ross, “Investigation of simulated annealing, ant-colony optimization, and genetic algorithms for self-
structuring antennas,” IEEE Trans. Antennas Propagat., vol. 52, no. 4, pp. 1007–1014, Apr. 2004.
25 of 54

26. A Novel Approach: The FFD­Based Method
Use DOE to handle the SSES problem z
σ(θin, θopt)
The SSES problem can be viewed as a process θ opt
θin
Input factors: 30 switches
Chosen level of input factors: 2 states
Output response: σ(θin, θopt) x
Why considering the problem as a process?
By performing a properly-designed experiment, the effect of each switch and their
interactions can be obtained
What is a “properly-designed” experiment?
A resolution-V fractional factorial design
Minimum aberration Xu’s 230–20 design
Minimum number of experimental trials
H. Xu, “Algorithmic construction of efficient fractional factorial designs with large run sizes,” Technometrics, vol. 51, no. 3, pp. 262–277,
Aug. 2009.
26 of 54

27. “Effects” of the Switches
s26 Variation of σ(θin, θopt)
Main
effect ……
s27
(30) E1 E2 E26 E27 E28 E30
s28
Two-factor … …
s29 interactions
(435) E12 E13 E14 E15 E26,27 E26,28 E29,30
s30
Three-factor
interactions ……
(4060) E123 Sparsity-of-effects principle E28,29,30
E124 E125 E26,27,28
Three-factorEffect
Two-factor interaction
Main interaction
TheThe on/off of σ(θof θ27 )
The change statesin, s opt
on/off state of 28 would Xu’s 230–20 design provides us
…
produced by two-factor in
affect the a change
would affect the main Higher-order interactions
unique estimation of all the
interaction s26, and vice s26 s27
the on/off state of and
effect of between s26 versa main effects and interactions
Thirty-factor interaction
27 of 54

28. Formulate the Effect Estimations into a COP
The designed 1024 experiments give us great information!
Estimate the effect and interactions
Calculate the estimation of 30 main effects Ei
and the 435 two-factor interactions Eij
Significance inference A combinatorial optimization
Only Identify the influential effects problem (COP)
This helps us investigate the efficiency of GA
Minimize z = ∑ Ei si + ∑∑ Eij si s j
i i j
Subject to si ∈ {−1, 1} , i = 1, 2,...30
Solve the associated COP
Use shotgun hill climbing to solve the COP
Stop when z does not improve for 100 iterations
28 of 54

29. Experimental Setup
SSES
NI connected cable
template
Post-processing of the RCS Bistatic RCS pattern
Pr ( 4π ) Rt Rr
2 2 3
σ= Polarization: VV
Pt Gt Gr λ 2 Front aspects –90°≤ θt ≤ 90°
Frequency: 1.5 GHz Sampling intervals: discrete steps
Rt = Rr = 2 m; Gt = Gr = 6 dBi of 5°
The experimental setup was co-worked with Yao-Chia Chan in 2011. 29 of 54

30. Original Performance
PEC plate of the same size Normal Incidence (θin = 0°)
Max: 8.56 dB
z Oblique Incidence (θin = 30°)
σ(θin, θopt)
θ opt
Max: 6.85 dB
θ in
x
30 of 54

31. RCS Reduction: Normal Incidence
Measurement
environment
y-polarized
incident wave
Normal
incidence
The optimum
result was found
after 1024 runs Results:
RCS reduction > 51 dB for θopt = 10°–90°
When θopt = 0°, the 140°-phase is insufficient to produce a null
31 of 54

32. RCS Reduction: Oblique Incidence
Measurement
environment
y-polarized
incident wave
Oblique
incidence
(θin = 30°)
The optimum
result was found Results:
after 1024 runs RCS reduction > 52 dB for θopt = 10°–90°
32 of 54

33. RCS Reduction: Oblique Incidence
Measurement
environment
y-polarized
incident wave
Oblique
incidence
(θin = 30°)
The optimum
result was found Results:
after 1024 runs RCS reduction > 46 dB for θopt = 0°–(–90°)
When θopt = –30°, the SSES failed to produce a null
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34. RCS Enhancement: Normal Incidence
Normal incidence (θin = 0°)
θopt = 0° θopt = 10° θopt = 15° θopt = 20°
RCSE=4.36 dB RCSE=3.46 dB
RCSE=2.39 dB RCSE=1.06 dB
Direct to a direction
Normal large angle
The case θopt = 20° failed to steer beam to the The external
Constructive
desired direction
interferenceis
phase
insufficient
We will develop a better element in the future
34 of 54

35. RCS Enhancement: Oblique Incidence
Oblique incidence (θin = 30°)
θopt = –30° θopt = 0° θopt = 15° θopt = 30°
RCSE=3.3 dB RCSE=0.3 dB
RCSE=1.9 dB
RCSE=3.96 dB
When the incident wave comes from an oblique direction, SSES can steer
beam to desired angles
It’s useful for reflectarray application fed with an offset antenna
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36. Evaluation of Algorithms
RCSR for normal incidence RCSR for oblique incidence
θopt = 30° θopt = 60°
The FFD-based method has similar performance as those found by GA,
but it reduce 83% of processing time, why?
The number of influential switches in SSES problem is only 10–20, but insignificant switches
share the genetic operators with equal chances
As the number of switches used increases, the efficiency of GA would degrade more drastically
36 of 54

37. Agenda
Motivation
Introduction
Contribution of this dissertation
RFID Antenna Conventional limitations
Application A novel tag structure and its validity
RCS Control Adaptive RCS control
Application Self-structuring electromagnetic scatterer
Design Tool An automatic antenna design tool
Application Wide- and multi-band antenna designs
37 of 54

38. Pixelized Design Technique
Handset Conventional design approach
Pixelized design approach
System environment
ground Antenna pixels
0 1 1 1 1 L0
3
0 0 1 1 1 0
0 1 1 1 1 1 1 1 1 1 1 0
w3
0 1 0 0 0 0 0 0 0 0 1 0
Specified design space
0 1 0 0 0 0 d
01 0 0 0 1 0
Design
0 1 0 0 0 1 0
0L11 L2
space 1 0 w4 0 0 1 0
LCD 0 1 0 0 0 0 1 1 0 1 1
w w
FR4 EncodeMinimize 2max(|S11|j)bitstream,
The antenna topologyat
the 1pixel states into a is
Iron bar
and perform= 890 and 1940 MHz as GA
freqj search algorithms such
automatically found
Assign proper objective
Identify the design space
function(s)
Form a solution domain of Perform binary optimization
size 2N by pixelization algorithm
M. P. Bendsøe and N. Kikuchi, “Generating optimal topologies in structural design using a homogenization method,” Comput. Methods in Appl.
Mech. Eng., vol. 71, pp. 197–224, 1988.
38 of 54

39. Historical Perspective
Pixels
Pixels (ON/OFF)
Ground
(ON/OFF)
Ground plane
plane
Patch antennas Planar monopole antennas
In 1997, the pixelized design Some literatures extended the
technique was first applied to technique to wideband planar
patch antenna designs monopole antenna designs
After that, most of the researches Both GA and PSO have been
focused on wideband or multiband shown that they are useful
patch antenna designs algorithms for this problem
These literatures focused on addressing particular test examples,
instead of extensively applying it to practical design situations
J. M. Johnson and Y. Rahmat-Samii, “A novel integration of genetic algorithms and method of moments (GA/MoM) for antenna design,” 1997
Applied Computational Electromagnetics Society Symposium Proceedings, Volume 2, Monterey, CA, March 17–21, pp. 1374–1381, 1997.
39 of 54

40. Why Do We Develop a Pixelized Design Tool?
We attempt to develop a competent pixelized design tool, which can
automatically design antennas and replace the conventional procedure
We lack a detailed investigation
5 on performance enhancement
Practical design situations
give specified design space
1
4
The literatures focused on
few design examples
2
The technique can develop
innovative antenna shapes
3 An automatic design tool can
shorten the design cycle
40 of 54

41. Implementations of the Pixelized Design Tool
We use the scripting interface provided by Ansoft,
performing a batch of predefined simulations and retrieve the simulated results
Matlab HFSS
Control and Functional
optimization Visual Basic Boundary
evaluations
conditions
1/0 Generate of materials
Assign configurations Launch HFSS
*.vbs
Perform a binary Simulation according
optimization algorithm to *.vbs
Evaluate performance Generate
Export analyzed results
measure Data *.m Any result at
matrix HFSS’s UI
This pixelized design tool is co-worked and compiled by Yao-Chia Chan in 2011–2012. 41 of 54

42. The Biggest Challenge of This Technique...
Metal Air
Air
Metal Air Pixels Pixels
An elaborate discretization
A non-uniform discretization
Require a huge number of pixels
Incorporate priori knowledge to
the discretization
The solution domain is sensitive to
design changes when the topology is
The number of decision variable
close to optimum significantly drops
But it is insensitive to design changes
The problem difficulty becomes
when the topology is far from much easier
optimum
A. Erentok and O. Sigmund, “Topology optimization of sub-wavelength antennas,” IEEE Trans. Antennas Propagat., vol. 59, no. 1, pp. 58–69,
Jan. 2011.
42 of 54

43. Investigation of Single­Objective Operation
GA and BPSO are implemented for
handling distinct problem natures
Degrees of exploration and exploitation Tradeoff between effort and efficacy
Solution space Solution space
To find a satisfactory performance, it
Optimum
requires about 20 hours for 2000
so far functional evaluations
Exploration Exploitation
The ideal population size in pixelized
In pixelized design problems, the design problems are found to be
degree of exploration needs to be around 32–64
emphasize a little bit
A. Colorni, M. Dorigo, F. Maffioli, V. Maniezzo, G. Righini, and M. Trubian, “Heuristics from nature for hard combinatorial optimization
problems,” International Transactions in Operational Research, vol. 3, no. 1, pp. 1–21, 1996.
43 of 54

44. Validation of Multiobjective Operation
Objective space
Pareto optimization
f2 (Objective #2)
A Pareto-based multiobjective evolutionary
algorithms identifies the nondominated set Pareto
front
Four optimizers are implemented:
NSGA-2, SPEA2, NSPSO, c-MOPSO
f1 (Objective #1)
All the performance measures in the user interface of HFSS can be extracted,
such as S parameters, antenna gain, and radiation efficiency
Minimize |S11| and |S21| at 900 MHz
The operations are validated by:
Air
Pixels
Pixels #: 57
44 of 54

45. Example : A MIMO Antenna System
Pareto front of each algorithms c-MOPSO SPEA2
Benefit
The decoupling is achieved by modification of the
antenna structure
Limitation
More human intervention is required to achieve
wideband operation
45 of 54

46. Wide­ and Multi­Band Pixelized Antenna Designs
Internal antenna designs for handset application
40 mm
Handset environment
Design space 15 mm International standards
An internal antenna design of F LTE700 DCS
available area 40 × 15 mm2 F: Feed point
GSM850 PCS
GSM900 UMTS
Ground 85 mm
Air plane
Two wide operational bands
Pixels 0.8-mm-thick Lower band: 698–960 MHz
Pixels #: 57
FR4 substrate Higher band: 1.71–2.17 GHz
Conventional wide- and multi-band antenna designs use
two objective functions: Max(|S11|j) and sum(|S11|j,dB)
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47. Conventional Objective Functions: Max(|S11|j)
Minimize the maximum |S11| of sample frequencies
Motivation: Since the worst |S11| is improved iteration after iteration, a safe performance
should be obtained and the BW should be enlarged
Strategy: Sampling the center frequencies of two bands or uniformly sampling in two bands
Sampling at two center frequencies
Due to the Bode-Fano criterion, a good matching
at a single frequency leads to severe impedance
variation at the adjacent frequencies
Interested bands:
698–960 and 1710–2170 MHz
Uniformly sampling at two bands
Sample |S11|
Winner
frequency 1 2 3 4 5
Candidate1 0.7 0.7 0.7 0.7 0.8 ^__^
Candidate2 0.1 0.1 0.1 0.1 0.9 >.<
N. Jin and Y. Rahmat-Samii, “Parallel particle swarm optimization and finite-difference time-domain (PSO/FDTD) algorithm for multiband and
wide-band patch antenna designs,” IEEE Trans. Antennas Propagat., vol. 53, no. 11, pp. 3459–3468, Nov. 2005.
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48. Conventional Objective Functions: Sum(|S11|j,dB)
Minimize the sum of |S11|dB among sample frequencies
Motivation: Since the area of the physical quantity, namely |S11|dB, is minimized, the BW
should be enlarged
Strategy: Sampling the center frequencies of two bands or uniformly sampling in two bands
Sampling at two center frequencies
and Uniformly sampling at two bands
Interested bands:
698–960 and 1710–2170 MHz The higher band is typically easier to
achieve
the exceedingly superior |S11|dB in the
higher band nullify other worse values
The algorithm is guided to exploit an
improper solution sub-domain
Z. Li, Y. E. Erdemli, J. L. Volakis, and P. Y. Papalambros, “Design optimization of conformal antennas by integrating stochastic algorithms
with the hybrid finite-element method,” IEEE Trans. Antennas Propagat., vol. 50, no. 5, pp. 676–684, May 2002.
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49. General Rules of Objective Functions
In fact, max(|S11|j) and sum(|S11|j,dB) come from the same general rule!
Sum(|S11|kj ) Sum[ (logk|S11|)j ]
max... |S11|10... |S11|5... |S11|2 |S11| log2|S11|... log5|S11|... log10|S11|... min
As k increase (for sum(|S11|kj )) As k increase (for sum[(logk|S11|)j ])
Wide band Narrow band
Poor matching Good matching
Concerning the worst case Concerning the best case
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50. Verification of the Statement
Interested band: Lower band (700–960 MHz) Sample frequency j: 700, 720, 740, ...,960 MHz
Sum(|S11|kj ) Sum[ (logk|S11|)j ]
Another verification for Sum(|S11|kj )
Laptop environment
Design space: 60 × 8 mm2
# of pixels: 53
824–960 and 1710–2170 MHz
Uniformly sampling 3 points in
each band
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51. A Novel Approach: A Multiobjective­Based Method
Use multiobjective framework to optimize one single measure
Motivation: Treating the wide and multiple bands as different objectives; within objective we
intend to have good matching, and between objectives we intend to have wide BW
Within objective: Apply sum[(logk|S11|)j ] with large k over chosen sample frequencies
Between objectives: Apply sum(|S11|kj ) with large k for summarizing a final performance measure
Within objective Between objectives
fi = Sum[(log7|S11|)j ] F = Max(fi)
～
～
698
960
1700
Lower band
2200
Higher band (MHz)
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52. The Optimal Design
Primary antenna topology Optimal design
40 mm
Conductor
15 mm
Air
F
Ground plane
Second-stage pixelized design was performed on
the junctions and edges of the primary design
Benefit
The wide and dual band is achieved automatically
The proposed approach outperforms conventional
Interested bands:
objective functions
698–960 and 1710–2170 MHz
Limitation
The |S11| for the lower band is only < –4.3 dB
Incorporating a multi-resonance structure for the
lower band into the discretization might help
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53. Summary of Today’s Presentation
Significance Implementation Performance
A dual-antenna The new tag structure DOE achieves multiple Isolation > 40 dB
structure for tags optimizes both reception design considerations Continuous reception
and signal backscattering within 0.1 × 0.1 λ02 Larger detection range
SSES It’s the first reconfigurable A prototyping SSES RCSR > 50 dB
reflective surface for system was successfully RCSE > 3.3 dB (normal)
adaptive RCS control built at NTU FFD outperforms GA
A pixelized The tool can cover
It handles multiobjective Various evolutionary
design tool 698–960 and 1710–
tasks and wide- and multi- algorithms were
2170 MHz within 40 ×
band antenna designs implemented
15 mm2 automatically
Optimization techniques act as the brain
of these applications!
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54. Future Work
Efficacy enhancement of the pixelized design tool
The initialization of the design space
A good solution domain should be constructed by: Design space
Physics of resonance requirement
Meander technique in the available area
Wide- and multi-band mechanisms
so that the solution domain is very diverse with promising solutions!
Dynamic-parameter mechanisms in GAs
Parallel processing of functional evaluations
We attempt to build a robust and practical design tool,
replacing the conventional design procedures!
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