Interactive, Procedural Computer-Aided Design

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Interactive, Procedural Computer-Aided Design
CAD/Graphics, Hong Kong, Dec. 7-10, 2005

• Carlo H. Séquin
• EECS Computer Science Division
• University of California, Berkeley

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CAD Tools for the Early and Creative Phases of
Design

• Tutorial
• E-CAD Examples
• ? Lessons for M-CAD, CAGD

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Outline
I. The Power of Parametric Procedural Design

• Parametric Procedural Design
• Computer-Aided Optimization / Synthesis
• CAD Tools for the Early Phases of Design
• Evolution (G.A.) versus Intelligent Design
• Towards an Integrated CAD Environment

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Julia Sets, Mandelbrot Set, Fractals
Defined by just a few numbers ... ?
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Sculptures by Brent Collins (1980-94)
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Sculpture Generator I Basic Modules
Normal biped saddles
Generalization to higher-order saddles(monkey
saddle)
Scherk tower
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Closing the Loop
straight or twisted
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Sculpture Generator I, GUI
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Brent Collins Hyperbolic Hexagon II
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12-foot Snow Sculpture

• Silver medal, Breckenridge, Colorado, 2004

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. . . and a Whole Lot of Plastic Models
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Bronze Sculpture

• Done by investment casting from FDM original

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Natural Forms by Albert Kiefer, sent by Johan
Gielis, developer of supergraphx

• made with supergraphx www.genicap.com

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The genome is the ultimate parameterization of
a design,given the proper procedureto interpret
that code

• Without the proper framework, the genome is
  meaningless. (e.g., human DNA on a planet in
  the Alpha-Centauri System)

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ProEngineer

• Parametric design of technical objects
• This captures only its form What about its
  function ?

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What Shape Has the Right Functionality?
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How Do We Know What Makes a Good Design With
Proper Functionality ?
e.g. a comfortable razor ? or a better mouse-trap
?

• Traditional Approach Trial and Error (TE)

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TE OK for Early Flying Machines
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TE Not OK for Nuclear Power Plants

• OK ! this one seems to work !!

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CAD for Design Verification

• Do expensive or dangerous experiments on the
  computer.
• Use calculations, analysis, simulation...
• E.g., SPICE (Simulation Program with Integrated
  Circuit Emphasis),L. W. Nagel and D. O. Pederson
  (1972)

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SPICE Input Circuit Diagram
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SPICE Output Voltage Current Traces
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Heuristics Analysis Programs? Computer-Aided
Synthesis

• Generate new designs based on well-established
  heuristics.
• Use evaluation CAD tools in an inner loop.
• Now Parameterize the desired function.
• First proven in domain of modular circuits (logic
  circuits, filters, op-amps ...)

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Parameterized Functional Specs

• Parameters for a band-pass filter

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Parameterized Filter Synthesis

• H. De Man, J. Rabaey, P. Six, L. Claegen,
• CATHEDRAL-II A Silicon compiler for Digital
  Signal Processing, 1986.

Architecture of dedicated data path
16-tap symmetrical filter
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Add Computer-Aided Optimization

• Use evaluation CAD tools
• a local optimization step
• as an inner loop in a search procedure.

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OPASYNA Compiler for CMOS Operational
AmplifiersH.Y. Koh, C.H. Séquin, P.R. Gray, 1990

• Synthesizing on-chip operational amplifiers to
  given specifications and IC layout areas.
• 1. Case-based reasoning (heuristic
  pruning)selects from 5 proven circuit
  topologies.
• 2. Parametric circuit optimization to meet specs.
• 3. IC layout generation based on macro cells.

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MOS Operational Amplifier (1 of 5)

• Only five crucial design parameters !

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Op-Amp Design (OPASYN, 1990)

• Multiple Objectives
• output voltage swing (V)
• output slew rate (V/nsec)
• open loop gain ()
• settling time (nsec)
• unity gain bandwidth (MHz)
• 1/f-noise (VHz-½)
• power dissipation (mW)
• total layout area (mm2)

Cost of Design weighted sum of deviations
Optimization minimize cost
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OPASYN Search Method
Fitness (GOOD)
Cost(BAD)

• 5D design-parameter space

Regular sampling followed by gradient ascent
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MOS Op-Amp Layout

• Following circuit synthesis optimization,
  other heuristic optimization procedures produce
  layout with desired aspect ratio.

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Synthesis in Established Fields

• Filter design and MOS Op-Amp synthesishave
  well-established engineering practices.
• Efficiently parameterized designs as well
  asrobust and efficient design procedures exist.
• Experience is captured in special-purpose
  programs and used for automated synthesis.
• But what if we need to design something new in
  uncharted engineering territory ?

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Uncharted Territory

• Task Design a robot that climbs trees !
• How do you get started ??

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An Important New Phase is Prepended to the
Design Process

• Idea Generation, Exploration ...

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Three Phases of Design

• Exploration -- Generating concepts
• Sanity Check -- Are they viable ?
• ? Schematic Design
• Fleshing out -- Considering the constraints
• Optimization -- Find best feasible approach
• ? Detailed Design
• Design for Implementation -- Consider
  realization
• Refinement -- Embellishments
• ? Construction Drawings

I
II
III
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Quality / Maturity of CAD Tools

• Gathering ideas, generating concepts
• POOR
• ? Schematic Design
• Considering constraints, finding best approach
• MARGINAL
• ? Detailed Design
• Refinement, embellishments, realization
• GOOD
• ? Construction Drawings

I
II
III
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Activities in Phase I

• How do people come up with new ideas ?
• Doodles, sketches, brain-storming, make
  wish-lists, bend wires, carve styrofoam, ...
• What CAD tools do we need to help ?
• Create novel conceptual prototypes ...
• Evaluate them, rank order them ...
• Show promising ones to user How do we automate
  that search ?

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Holey Fitness Space

• Open-ended engineering problems have complicated,
  higher-dimensional solution / fitness spaces.

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Genetic Algorithms

• Pursue several design variations in
  parallel(many phenotypes in each generation)
• Evaluate their fitness (how well they meet the
  various design objectives ? Pareto set)
• Use best designs to breed new off-springs(by
  modifying some genes mutation)(by exchanging
  genes crossover)
• Expectation Good traits will survive,bad
  features will be weeded out ...

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How Well Do G.A. Work for Engineering Tasks
?An Experiment

• Let ME students design a MEMS resonator
• Students (initially) had no IC experience
• Good programmers
• Excited about Genetic Algorithms

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Micro-Electromechanical SystemsMEMS

• Created with an enhanced fabrication technology
  used for integrated circuits.
• Many nifty devices and systems have been built
  motors, steerable mirrors, accelerometers, chemo
  sensors ...

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MEMS Example

• Ciliary Micromanipulator,K. Böhringer et al.
  Dartmouth, 1997.

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The Basics of a MEMS Resonator

• Filters
• Accelerometers
• Gyroscopes

Prevent horizontaloscillations !
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Basic MEMS Elements (2.5D)

• Beam

• H-shaped center mass

Comb drive
Anchor to substrate
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Need an Electro-Mechanical Simulator !

• SUGAR
• SPICE for the MEMS World
• (open source just like SPICE)

DESIGN
fast,simple,capable.
MEASUREMENT
SIMULATION
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The SUGAR Abstraction

• Digital-to-Analog Converter by R. Yej, K.S.J.
  Pister

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SUGAR in Action ...

• Multimode Resonator by R. Brennen

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A General Set-Up for Optimization

• Poly-line suspensions at 4 corners.

• Adjust resonant frequency F
• Bring Kx Ky into OK ranges
• Minimize layout area

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An Intermediate Design/Phenotype

• Adjust resonant frequency to 10.0 0.5 kHz
• Bring Kx / Ky into acceptable range ( gt10 )
• Minimize size of bounding box core is fixed.

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MEMS Actually Built and Measured
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Genetic Algorithm in Action !

• Area 0.181 mm2 Kx/Ky 12

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Use 4-Fold Symmetry !

• 1st-order compensation of fabrication variations

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Using 4-fold Symmetry

• Faster search ! Area 0.171 mm2 Kx/Ky 12

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X,Y-Symmetry Axis-Aligned Beams

• Area 0.211 mm2 Kx/Ky 118

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Introduce Serpentine Element
Wv
Wh
Lv
N3
Lh

• A higher-order composite subsystemwith only five
  parameters N , Lh, Wh, Lv, Wv

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X,Y-Symmetry Mixed Springs

• Area 0.149 mm2 Kx/Ky 13

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Proper Use of Serpentine Sub-Design

• That is what we had in mind ...

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Proper Use of Serpentine Element

• Area 0.143 mm2 Kx/Ky 11

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Trying to Reduce Area

• Area 0.131 mm2 Kx/Ky 4 ? BAD !

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Increasing Stiffness Kx

• Connecting bars suppress horizontal oscillations
• But branched suspensions may not be expressible
  in genome ( underlying data structure ).

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Using Cross-Linked Serpentines
PROFESSIONAL DESIGN

• Area 0.126 mm2 Kx/Ky 36

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What really happened here ?

• Major improvement steps came by engineering
  insights.
• Genetic algorithm found good solutions for the
  newly introduced configurations.
• With only few parameters clear objectives,
  greedy optimization may be more efficient.
• With complex multiple objectives, G.A. may have
  advantage of parallel exploration.

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Why Did the G.A. Not Find This ?

• Lack of expressibility of genome.
• Solution space too large, too rugged ...
• Sampling is too sparse !
• Samples are not driven to local optima.

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A Rugged Solution Space

• No design lies on the very top of a peak !
• Good intermediate solutions may get lost.

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What Are Genetic Algorithms Good For?

• Exploring unknown territory
• Generating a first set of ideas
• Showing different subsystem solutions

How can this be harnessed most effectively in an
engineering design environment ?
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Current Work
Building a flexible, extensible CAD framework
for exploration, ideation, design, and
optimization. Test MEMS Resonators, Filters,
Gyroscopes

• With
• Prof. A. Agogino (ME)
• Dr. Raffi Kamalian
• Ying Zhang, PhD student
• Corie Cobb , PhD student

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Making G.A. Useful for Engineering

• G.A. by itself is not a good engineering tool !

Selection ofgood startingphenotypes
Visualization
Suggestiveediting
G.A.
Selectivebreeding
GreedyOptimization
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G.A. for Engineering Needs (1)

• A way to pick promising initial designs,e.g.
  from
• a case library
• classical literature search
• internet searches
• personal advice from experts
• sketches, doodles

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Our Component / Case Library

• Multiple levels of building blocks
• Low-level primitive design elementanchors,
  masses, beams, combs ...
• High-level design clustersI masses,
  polylines, serpentines ...
• Successful designs (Case Library)mechanical
  resonators ...

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G.A. for Engineering Needs (2)

• An extensible underlying data structure,
• compatible with the available simulator (SUGAR) !
• Fixed Structured descriptions
• Sculpture Generator I fixed set of parameters
• OPASYN a tree of 5 basic designs (5-8 params.)
• ? too rigid
• Grammar-based representations
• Lindenmayer Systems (1968) parallel
  string-rewrite
• Artificial Life by Karl Sims (1991).

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Hierarchical MEMS

• Frequency-Selective MEMS for Miniaturized
  Communication Devices
• Clark T.-C. Nguyen, Proc. 1998 IEEE Aerospace
  Conf.

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C.T.-C. Nguyen MEMS Filter
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C.T.-C. Nguyen 3-Resonator Filter

• MEMS
• Cuircuit

Electro-mechanical analogy
Exchange only modules at the same hierarchical
level !
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Our Representation of Designs

• Object-oriented (C) hierarchical graph
• modules with connection points
• connectivity via net list.
• Parameter set of building blocks act as genes
• real, integer, and binary numbers.
• Other fields indicate allowable modifications
• what can mutate, by how much
• which elements can perform genetic crossover?
  respecting hierarchical levels !

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G.A. for Engineering Needs (3)

• Efficient ways to predict the functionality and
  fitness of phenotypes
• simulator for the appropriate domain (SUGAR)
• heuristic evaluations based on past experience
• visualization for quick human judgment? keeping
  common-sense control !

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G.A. for Engineering Needs (4)

• Ways to improve the evolutionary process
• greedy phenotype optimization
• deletion / advancement of special phenotypes
• introducing new parameters / constraints
• high-lighting of desirable features . . . ?

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Modeling by Example

• T. Funkhouser et.al, Princeton, Siggraph 2004

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G.A. for Engineering Needs (5)

• Ways to edit individual designs
• sketching a whole new systems topology(this may
  be a far-out dream ...)
• selective editing of phenotypesstory-board
  visualization of the sought-after design
  environment . . . ?

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Design Example MEMS Accelerometer

• G.A. constrained to Manhattan geometry,
• and 4-fold
  symmetry.

area 0.145 mm2
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Accelerometer (cont.)

• Added serpentine elements

area 0.138 mm2
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Accelerometer Result

• New, more compact serpentine (fewer params)

area 0.113 mm2
Do we really need G.A. to find this solution ??
We definitely need engineering intelligence !
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Summary

• CAD will not become fully automated anytime
  soon.
• Human intelligence will continue to play a key
  role
• engineering experience
• common sense
• It must be more tightly integrated into the
  design process
• ? faster design completion
• ? better design results

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Todays CAD Environments for Phase I

• corresponding state of the art ...

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CAD Environments of the Future

• Phase_1 CAD tools have a long way to go yet !
• Encourage bright young minds to work in this
  field.

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QUESTIONS ?
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Interactive CAD for Phase I
GeneticAlgorithms
GradientDescent
CaseLibrary
HumanIntelligence
SynthesisFramework
GraphicalInterface