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Interactive, Procedural Computer-Aided Design

CAD/Graphics, Hong Kong, Dec. 7-10, 2005. Interactive, Procedural Computer-Aided Design. Carlo H. Séquin EECS Computer Science Division University of California, Berkeley. CAD Tools for the Early and Creative Phases of Design. Tutorial E-CAD Examples  Lessons for M-CAD, CAGD.

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Interactive, Procedural Computer-Aided Design

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  1. CAD/Graphics, Hong Kong, Dec. 7-10, 2005 Interactive, Procedural Computer-Aided Design Carlo H. Séquin EECS Computer Science Division University of California, Berkeley

  2. CAD Tools for the Early and Creative Phases of Design Tutorial E-CAD Examples  Lessons for M-CAD, CAGD

  3. Outline • 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 I. The Power of Parametric Procedural Design

  4. Julia Sets, Mandelbrot Set, Fractals Defined by just a few numbers ... 

  5. Sculptures by Brent Collins (1980-94)

  6. “Sculpture Generator I” – Basic Modules Normal “biped” saddles Generalization to higher-order saddles(monkey saddle) Scherk tower

  7. Closing the Loop straight or twisted

  8. These parameters define sculpture; = “genome” “Sculpture Generator I”, GUI

  9. Brent Collins & Hyperbolic Hexagon II

  10. 12-foot Snow Sculpture Silver medal, Breckenridge, Colorado, 2004

  11. . . . and a Whole Lot of Plastic Models

  12. Bronze Sculpture Done by investment casting from FDM original

  13. “Natural” Forms by Albert Kiefer, sent by Johan Gielis, developer of supergraphx • made with supergraphx www.genicap.com

  14. 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)

  15. ProEngineer • Parametric design of technical objects • This captures only its form – What about its function ?

  16. What Shape Has the Right Functionality?

  17. How Do We Know What Makes a Good Design With Proper Functionality ? • Traditional Approach:Trial and Error (T&E) e.g. a comfortable razor ? or a better mouse-trap ?

  18. T&E: OK for Early Flying Machines

  19. T&E: Not OK for Nuclear Power Plants OK ! – this one seems to work !!

  20. 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)

  21. SPICE – Input: Circuit Diagram

  22. SPICE Output: Voltage & Current Traces

  23. 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 ...)

  24. Parameterized Functional Specs Parameters for a band-pass filter

  25. 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

  26. Add: Computer-Aided Optimization • Use evaluation CAD tools + a local optimization step as an inner loop in a search procedure.

  27. 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.

  28. MOS Operational Amplifier (1 of 5) Only five crucial design parameters !

  29. 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 (V*Hz-½) • power dissipation (mW) • total layout area (mm2) “Cost” of Design = weighted sum of deviations Optimization: minimize cost

  30. Hard design constraints OPASYN Search Method Fitness (GOOD) 5D design-parameter space Cost(BAD) Regular sampling followed by gradient ascent

  31. MOS Op-Amp Layout • Following circuit synthesis & optimization,other heuristic optimization procedures produce layout with desired aspect ratio.

  32. 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” ?

  33. Uncharted Territory • Task: Design a robot that climbs trees ! • How do you get started ??

  34. An Important New Phase is Prepended to the Design Process: Idea Generation, Exploration ...

  35. The CAD Wave Three Phases of Design I • 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 II III

  36. Quality / Maturity of CAD Tools I Gathering ideas, generating concepts • POOR  Schematic Design Considering constraints, finding best approach • MARGINAL  Detailed Design Refinement, embellishments, realization • GOOD  Construction Drawings II III

  37. 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 ?

  38. “Holey” Fitness Space • Open-ended engineering problems have complicated, higher-dimensional solution / fitness spaces.

  39. 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 ...

  40. 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

  41. 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 ...

  42. MEMS Example • Ciliary Micromanipulator,K. Böhringer et al. Dartmouth, 1997.

  43. The Basics of a MEMS Resonator • Filters • Accelerometers • Gyroscopes Prevent horizontaloscillations !

  44. Basic MEMS Elements (2.5D) Beam H-shaped center mass Anchor to substrate Comb drive

  45. Need an Electro-Mechanical Simulator ! “SUGAR” “SPICE for the MEMS World” (open source just like SPICE) DESIGN fast,simple,capable. MEASUREMENT SIMULATION

  46. The SUGAR Abstraction Digital-to-Analog Converter by R. Yej, K.S.J. Pister

  47. SUGAR in Action ... Multimode Resonator by R. Brennen

  48. 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

  49. An Intermediate Design/Phenotype • Adjust resonant frequency to 10.0 ± 0.5 kHz • Bring Kx / Ky into acceptable range ( >10 ) • Minimize size of bounding box; core is fixed.

  50. MEMS Actually Built and Measured

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