Simulation Driven Product Development for Electromagnetic - Ansys

ANSYS Solution: 

Simulation Driven 

Product Development 

for Electromagnetic 

Applications 

Jeff Tharp, Ph.D. 

© 2009 ANSYS, Inc. All rights reserved. 1 ANSYS, Inc. Proprietary

Outline 

• Introduction 

• Integration 

• Product Design Flow 

• Simulation Driven Product Development (SDPD) 

– SMA example 

• Conclusions 


“What If?” 

• Where Achieving is the a good best solution? design often means making 

tradeoffs… • Performance ”What is If” scenarios 

– A optimized, good design but requires can not only that the 

performance it be reliablyquantity 

is optimized 

manufactured? 

• Performance must be maintained over expected 

• Will use parameter SMAvariations 

connector – Must know tohow 

multi-variable variation 

demonstrate impacts performance flow 

• Enables you to answer the “what-if” questions so 

the right decisions can be made based on accurate 

information 


Introduction 

• The release of HFSS12 and Workbench 12 

enables opportunity to enhance way engineers 

answer these questions 

• ANSYS Workbench 

– Intuitive multiphysics 

layout 

• HFSS12 

– Advanced EM extraction 

tool 


Introduction: DesignXplorer 

• DesignXplorer (DX) is a tool for designing and 

understanding the analysis response of parts and 

assemblies 

–Account for uncertainties in product design 

• Six Sigma Analysis 

–Intuitively optimize a given problem 

• Goal Driven Optimization 

• DX explores a wide range of responses based on a 

limited number of actual solutions. 

–Uses Design of Experiments (DOE) 

–Functions with any input/output combination 

–Now including Ansoft products! 



• The Response Surface Method allows for optimization and sixsigma 

studies efficiently 

• DX uses Design of Experiments (DOE) 

– DOE method determines how many, and which, design points 

should be solved for the most efficient approach to optimization 

– Many different DOE methods 

– Homogeneous and Inhomogeneous distributions 

– Response surface is fit to solved DOE 



• EE’s are familiar with optimizations and 

benefits of Response Surfaces 

• What is Six-Sigma-Analysis? 

• Statistical analysis that takes the inherent 

tolerances of input parameters in a given 

component and determines the statistical 

distribution of the output parameters 

• What insight does this analysis bring? 

• Use Statistical Analysis to predict manufactured fail 

rates 

• Uses DOE and parameter tolerances to create a 

response surface 


Integration 

• DX is a desired tool for efficient design flow… 

• DOE analysis enhances HFSS and other Ansoft 

tools 

– All solution space fit and mapped 

• Many DOE methods 

– Improved optimization capability and design insight 

• DX is available for all physics 

– All ANSYS solvers, Ansoft solvers, and third party 

tools can be brought into DX for design exploration 

– Ansoft tools integrated into interface…no scripting! 


Integration 

• HFSS/DX integration is ideal combination 

• DX allows for FULL utilization of parametric analysis 

– Parametric analysis is efficiently guided by DX 

• Automated Adaptive Mesh ensures accurate solution 

for each variation…transparent to user 

– DSO allows for fast and DSO 

paralleled computation of the 

variations 


Simulation Driven Product 

Development (SDPD) Flow 

Design Variables 

-Input/Output 

Design of Experiments 

Response Surface 

Sensitivity 

Tradeoff 

Design Exploration 

Pareto Fronts 

Parameter 

Correlation 

Spider Plots 

Parallel Charts 

Optimization 

6σ Design of 

Experiments about 

Optimized Variation 

Response Surface 

Six Sigma Analysis 

Manufacture 


Simulation Driven Product 

Development (SDPD) Flow 

• Best way to demonstrate Simulation Driven 

Product Development Flow is through 

example 

• SMA Connector 


SDPD: SMA Optimization 

• SMA connector 

• Coax to microstrip 

transition 

• Goal is to minimize 

Return Loss at 12GHz 

• Manufacture for 6σ 

• 3 degrees of freedom 

Radius of transition pin 

[ 15 – 45 mil ] 

Length of transition pin 

[ 120 – 220 mil ] 

Antipad spacing 

[ 10 – 80 mil ] 


SMA : Max/Min and Optimization 

• Used an Optimal-Space-Filling Design DOE 

– Inhomogeneous distribution of design 

variations 

– Can set the number of variations manually 

• Used 64 variations for DOE 

• Exported the DOE to a parametric sweep in HFSS! 

– Solved the Parametric sweep using DSO on a 16 core 

machine 

– Set the machines to 

use and chose to run with no 

graphics 

– All within the WB12 interface! 



• Once DOE is solved, quickly see qualitative 

feedback of the solved points 

using the Parallel Parameters Plot! 

– Not based on response surface, just feedback 

from the solved 

points of the 

DOE! 



• First step in optimization: What is the 

best/worst performance globally?? 

– MAX/MIN search of DOE! 

– Begin with a screening optimization for ideal 

candidate 

designs 

based upon 

curve fit 



• Take the soft Candidate design point and 

explicitly solve for hard reference! 

• The hard design is good…but we can do better 

• How does the rest of solution space look? 

– Tradeoff plot 

– Note ability for Design 

Exploration! 

• Local minima observed 



• Specific Variable 

Tradeoff Plots 

• Alternative minima 

• Qualitative 

sensitivity from 

width of minima 

True Design Exploration!! 



• Refine Optimization using the Multi-Objective 

Genetic Algorithm (MOGA) 

Pushed HFSS Explicit Solution for Candidate A (excellent correlation) 

• Looks like an excellent optimized design! 

– BUT: Can it be reliably manufactured ??? 


SMA: Six Sigma Analysis 

• Determine the stability of the performance for 

optimized variation based on fabrication 

constraints 

– How reliable is this variation to mass produce 

and satisfy performance criteria using vendor 

6σ values for parameter tolerance? 

– How many failures per million manufactured? 

• Radius of transition pin ±5mils 

• Length of transition pin ±10mils 

• Spacing of Antipad ±2mils 

– Maximum allowed Return Loss -20dB 



• Intuitive setup 

– Enter the mean 

value of parameter 

– Enter Distribution Type 

– Enter the standard 

deviation 



• By using the vendor tolerances and applying a DOE in DX, can 

determine statistical expectations of performance! 

• In this case, 99.918% of SMA connectors will have Return Loss less 

than -20 dB! 

– Fail rate = 819 per million (3.15 σ) : Will need tighter vendor 

tolerances or other design for 3.4 per 1,000,000 (6σ) 

• 99.9934% will have RL less than -15dB (3.82 σ) -> 65.6 fail in 1 million 

• One gains much 

insight into the 

fabrication 

tolerances needed 

for desired 

manufactured 

performance! 



• The Six Sigma Sensitivity also gives intuitive perspective as to 

which variable the performance is most sensitive! 

– Which variable needs the highest tolerance component? 

• COST MANAGEMENT through design! 

Variations in the radius of the 

transition pin have the most impact 

on performance at the optimized 

variation! 


SMA: Optimization, DC to 15GHz 

• Same parameters and ranges as 12GHz case 

• Solved for three frequency ranges 

– DC – 5GHz, 5 – 10GHz, 

10 -15GHz, and whole band DC- 15GHz 

• Optimize Spectral Performance 



• Note the tradeoff of output variables of the 

reflection from DC to 5GHz and 10 – 15 GHz 

– As one gets better, other gets worse… 



• Pushed optimal variation to HFSS for hard 

reference 

• What are the design Sensitivities? 

DC to 5GHz 5 to 10GHz 10 to 15GHz 

DC to 15GHz 


SMA: Six Sigma, DC to 15GHz 

• What are statistics of connectors with Return 

Loss less than -12.5 dB over DC to 15 GHZ 

– Same vendor tolerances and single freq case 

– 98% pass 


Conclusions 

• Combining DesignXplorer with the 

capabilities of the Ansoft tools work together 

to give optimal design flow 

– Simulation Driven Product Development! 

• Complimentary combination 

– HFSS facilitates DOE, by solving parameter 

variations in parallel with DSO 

– Together… accurate and efficient solution for 

optimized Design Analysis and Exploration 


Conclusions 

• DX allows for total solution space analysis 

– A good design requires not only single 

optimized performance variation 

• In “real world” 

• Performance must be maintained over expected 

parameter variations due to vendor specs (SMA) 

– DX creates an accurate curve fit to represent 

the solution space with a minimum of solved 

response points!! 

• Faster and more robust optimizations 

• Much less likely to land in local minimum as whole 

solution space is represented 

• Accurate Six Sigma Analyses 


ANSYS Solution: 

Simulation Driven 

Product Development 

for Electromagnetic 

Applications 

Jeff Tharp, Ph.D.

Simulation Driven Product Development for Electromagnetic - Ansys

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