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© CADFEM 2015
Optimization of a Dual Band Antenna
using `Statistics on Structures`
Marc Vidal, Sebastian Wolff, Christian Römelsberger
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© CADFEM 2015
Internet of Things and Antennas
2
• Number of connected devices
growing rapidly
• ca. 30,000,000,000 devices by 2020!
• Automotive, Households, Industrial,
Consumer, Communications, …
• Wearable and mobile devices
wirelessly connected
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Challenges in Antenna Design
3
• Communication of wireless devices
through various frequency bands
using different technologies and
standards
• NFC
• WLAN
• Bluetooth
• ZigBee
• …
• Wearable and mobile devices should
be small
• Save space by using multiple band
antennas
Source: Wikipedia:
Nicolas Sadoc, Nick Pitsas, CSIRO
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Engineering Task:
Design antenna structure that works at
the given frequency bands and fits into
a given small space!
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HFSS – High Frequency Structure Simulator
• 3D Field Solver
• 3D Finite Element Method (FEM)
• Boundary Integral (IE)
• Mesh Process: Adaptive
• Advanced Boundary Types
• Radiation and Perfectly Matched Layers
• Symmetry, Finite Conductivity, Infinite Planes, RLC, and Layered Impedance
• Advanced Material Types
• Frequency dependent
• Anisotropic
• Post Processing and Report Type
• SYZ parameters
• Field display
• Near Field/Far Field
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Simulation Model – Dual Band Slot Antenna
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• Need to move minima of return loss
to the right frequencies
• Make 5.8GHz minimum deeper
FR4 Substrate
Slot
GND plane
Microstrip feed
on the back side
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• Which parameter shall be taken for a manual variation?
Simulation Model – Dual Band Slot Antenna
lf
wf 50Ω
ws
hs
w1
w2
gap1
gap2
dd
dd
gap1 gap2
ws w2 hs w1
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The Optimization Goal
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• Return Loss at 2.4GHz and at 5.8GHz minimal
• Find out after calculating 100 Designs:
• COP not good enough for optimization!
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Use of SoS
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• Better:
• Get signals from DOE and construct an F-MOP (Field Metamodel of Optimal
Prognosis)
• Be able to define optimization goal after DOE!
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Field Meta Model
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• Expand a signal in terms of ‘shape functions’
• ‘Shape functions’ are determined from the DOE of signals in order to capture
the most important characteristics of the signals
• ‘Regular’ MOP for the expansion coefficients z1, z2, z3,…
≈ *z1+ *z2+ *z3+ *z4+… +
Mean value of DOE
at each frequency
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Field Meta Model
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Determine
‘shape functions’
Calculate MOP
for coefficients
Reassemble
signals
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Plan / Workflow
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DOE
(optiSLang) SoS
Optimization
(optiSLang)
Validation
(HFSS)
Signals
F-MOP
for
Signals
Better
Design
•Fully
automated
sampling
•‘Shape
Functions’ for
signals
•F-MOP
•F-CoP
•Decide on
optimization
goal
•Use F-MOP as
raw data
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F-CoP
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• Bad F-CoP for return loss
Due to dB-Scale of
The Return Loss
(Singularity)
Bad explainability due
to little variation and little
correlation in frequency domain
combined with nonlinear effects
Good F-CoP
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F-CoP
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• But Return Loss is expressible in terms of complex S-parameters
• With good F-CoP for the S-parameters
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Reformulation of the Optimization Goal
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• Extract positions and values of the
minima of a given signal
(from F-MOP)
• Optimization Goal:
• Minimize Max(Min24,Min58)
• Constraints:
• Pos24=2.4
• Pos58=5.8
Pos24 Pos58
Min24
Min58
Range 1 Range 2
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Optimization using ARSM on F-MOP
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MOP for
Coefficients zi
Use ‘shape functions’
to generate F-MOP
Extract scalar
quantities as
optimization goals
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Summary
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• Good quality of prognosis for full
signals (e.g. Return Loss) using F-
MOP:
• No new simulations
• Possibility for plausibility checks of
solutions before validation using FE-
analysis
• Discover new possibilities (e.g. third
frequency band)
• CoP for signals over frequency (F-
CoP)
• Explain Relations
• Advantage for the engineer:
• Can define optimization goals after
DOE has been done and signals have
been understood.
• Generate F-MOP once and then look at
behavior of signals under optimization.
• No re-evaluation of DOE
F-CoP