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MEMS Design using Genetic Algorithms

CS 285. MEMS Design using Genetic Algorithms. Carlo H. Séquin EECS Computer Science Division University of California, Berkeley. Genetic Algorithms. Pursue several design variations in parallel (many phenotypes in each generation)

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MEMS Design using Genetic Algorithms

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  1. CS 285 MEMS Design usingGenetic Algorithms Carlo H. Séquin EECS Computer Science Division University of California, Berkeley

  2. 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 / combining their genes) • Expectation: Good traits will stick around,bad solution will be weeded out ...

  3. 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)

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

  5. Micro-Electromechanical SystemsMEMS • Created with a somewhat enhanced fabrication technology used for integrated circuits. • Many nifty devices and systems have been built: motors, steerable mirrors, accelerometers, chemo sensors ...

  6. The Design of a MEMS Resonator • Filters • Accelerometers • Gyroscopes Prevent horizontaloscillations ! Resonate vertically at desired frequency

  7. Basic MEMS Elements Beam H-shaped center mass Anchor to substrate Comb drive

  8. A General Set-Up for Optimization • Poly-line suspensions at 4 corners. • Adjust resonant frequency F • Get Kx Ky into OK ranges • Minimize layout area

  9. Need an Electro-Mechanical Simulator ! “SUGAR” “SPICE for the MEMS World” (open source just like SPICE) DESIGN Fast,Simple,Capable. MEASUREMENT SIMULATION

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

  11. MEMS Actually Built and Measured

  12. Genetic Algorithm in Action ! • Area = 0.181 mm2; Kx/Ky = 12

  13. Use 4-Fold Symmetry ! • 1st-order compensation of fabrication variations

  14. Using 4-fold Symmetry • Faster search ! Area = 0.171 mm2; Kx/Ky = 12

  15. X,Y-Symmetry; Axis-Aligned Beams • Area = 0.211 mm2; Kx/Ky = 118

  16. Introduce Serpentine Element • A higher-order composite subsystemwith only five parameters: N , Lh, Wh, Lv, Wv Wv Wh Lv Lh N=3

  17. Proper Use of Serpentine Sub-Design • That is what we had in mind ...

  18. Reduce X-dimension of layoutby introducing more serpentine loops Proper Use of Serpentine Element • Area = 0.143 mm2; Kx/Ky = 11

  19. soft Kx flare out Trying to Reduce Area • Area = 0.131 mm2; Kx/Ky = 4 !!

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

  21. Using Cross-Linked Serpentines • Area = 0.126 mm2; Kx/Ky = 36 PROFESSIONAL DESIGN

  22. Why Does 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.

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

  24. 20. Generation – drifting to higher ground 1. Generation – a random sampling 50. Generation – clustered near high mountains A Rugged Solution Space • No phenotype is on the top of a peak • Good intermediate solutions may get lost

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

  26. 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 ?

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

  28. Making G.A. Useful for Engineering Selection ofgood startingphenotypes Visualization • G.A. by itself is not a good engineering tool Suggestiveediting G.A. Selectivebreeding GreedyOptimization

  29. 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.

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

  31. Op-Amp Design (OPASYN, 1990) Multiple Objectives: • power dissipation (mW) • 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) • total layout area (mm2) “Cost” of Design = weighted sum of deviations

  32. Hard design constraints OPASYN Search Method Fitness Design-parameter space Cost Regular sampling followed by gradient ascent

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

  34. Synthesis in Established Fields • Filter design and MOS Op-Amp synthesishave well-established engineering practices. • Efficiently parameterized designs as wellasrobust 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 in “uncharted engineering territory” ?

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