Hybrid evolutionary optimal MEMS design

ORIGINAL ARTICLE

Abstract

A hybrid evolutionary design synthesis and optimization process for microelectromechanical systems (MEMS) devices has been developed. The process integrates a MEMS design component library with multiple simulation modules and two levels of design optimization: global multi-objective genetic algorithms (MOGA) and local gradient-based refinement. During the hybrid evolutionary design process, MOGA randomly searches the design space and approaches the desirable design solutions using probabilistic transition rules, and gradient-based local optimization refines promising design candidates with computational efficiency. To efficiently apply hybrid evolutionary optimization techniques on MEMS designs, a hierarchical tree-structured component-based genotype representation has been developed, which incorporates specific engineering knowledge into the design synthesis and optimization process. The MEMS design component library serves as a source of practical and efficient genotypes for the evolutionary process, with each component associated with its instructions and restrictions on genetic operations. The component-based genotype incorporated with engineering knowledge constrains evolutionary searching in appropriate and promising regions of the search space, allowing a deeper search in a given amount of time. Hybrid evolutionary MEMS design synthesis and optimization are demonstrated with surface-micromachined resonator and accelerometer designs.

Keywords

Hybrid evolutionary process Multi-objective genetic algorithm Microelectromechanical systems Design synthesis and optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

170_2012_3908_MOESM1_ESM.docx (12 kb)
ESM 1 (DOCX 11 kb)

References

  1. 1.
    Senturia SD, Harris RM, Johnson BP, Kim S, Nabors K, Shulman MA, White JK (1992) A computer-aided design system for microelectromechanical systems (MEMCAD). J Microelectromech Syst 1:3–13CrossRefGoogle Scholar
  2. 2.
    Crary S, Zhang Y (1990) CAEMEMS: an integrated computer-aided engineering workbench for micro-electrical-mechanical systems. Proceedings of the IEEE Workshop on Micro Electro Mechanical Systems (Napa, CA, February 1990) pp 113–114Google Scholar
  3. 3.
    Buser RA, de Rooij NF (1991) ASEP: a CAD program for silicon anisotropic etching (micromechanical structure). Sensors Actuators Phys 28(1):71–78CrossRefGoogle Scholar
  4. 4.
    Koppelman GK (1989) Oyster: a three-dimensional structural simulator for microelectromechanical design. Sensors Actuator 20:179–185CrossRefGoogle Scholar
  5. 5.
    Funk JM, Korvink JG, Buhler B, Bachtold M, Baltes H (1997) SOLIDIS: a tool for microactuator simulation in 3-D. J Microelectromech Syst 6(1):207–212CrossRefGoogle Scholar
  6. 6.
    Crary S, Zhang Y (1991) Software tools for designers of sensor and actuator CAE systems. Transducers’ 91, International Conference on Solid-State Sensors and Actuators, Digest of Technical Paper (Cat. No.91CH2817-5) pp. 498–501Google Scholar
  7. 7.
    Aluru NR, White J (1996) Direct Newton finite-element/boundary element technique for micro-electro-mechanical analysis. Presented at IEEE Solid-State Sensor and Actuator Workshop (Hilton Head, SC) pp. 54–57Google Scholar
  8. 8.
    Intellisuite (2011) http://www.intellisense.com. Accessed 27 Dec 2011
  9. 9.
    Wang F, White J (1998) Automatic model order reduction of microdevices using the Arnoldi approach. ASME International Mechanical Engineering Congress and Exposition, Proceedings of Microelectromechanical Systems (MEMS), (Anaheim, CA, Nov. 1998) pp 527–530Google Scholar
  10. 10.
    Vandemeer J, Kranz M, Fedder G (1997) Nodal simulation of suspended MEMS with multiple degrees of freedom. Robotics Institute, Paper 318Google Scholar
  11. 11.
    Zhou N, Clark JV, Pister KSJ (1998) Nodal simulation for MEMS design using SUGAR v0.5. International Conference on Modeling and Simulation of Microsystems Semiconductors, Sensors and Actuators (Santa Clara, CA, April 1998) pp 308–313Google Scholar
  12. 12.
    Clark JV, Zhou N, Pister KSJ (1998) MEMS simulation using SUGAR v0.5. Solid-State Sensor and Actuator Workshop (1998) pp 191–196Google Scholar
  13. 13.
  14. 14.
  15. 15.
    Li J, Gao S, Liu Y (2007) Solid-based CAPP for surface micromachined MEMS devices. Comput Aided Des 39(3):190–201CrossRefGoogle Scholar
  16. 16.
    Tahir FA, Aubert H (2011) Equivalent electrical circuit model for design and optimization of MEMS-controlled reflectarray phase sifter cells. Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP) pp 240–243Google Scholar
  17. 17.
    Haronian D (1995) Maximizing micro-electromechanical sensor and actuator sensitivity by optimizing geometry. Sensors Actuator Phys 50(3):223–236CrossRefGoogle Scholar
  18. 18.
    Fedder G, Mukherjee T (1996) Physical design for surface-micromachined MEMS. Proceedings of the 5th ACM/SIGDA Physical Design Workshop (Reston, VA, 1996) pp 53–60Google Scholar
  19. 19.
    Mukherjee T, Fedder G (1997) Structured design of microelectromechanical systems. Proceedings of 34th ACM Design Automation Conference (Anaheim, CA, 1997) pp 680–685Google Scholar
  20. 20.
    Iyer S, Mukherjee T, Fedder G (1997) Automated optimal synthesis of microresonators. Solid-State Sensors and Actuators (Chicago, IL 1997) p12-19Google Scholar
  21. 21.
    Mukherjee T, Iyer SV, Fedder G (1998) Optimization-based synthesis of microresonators. Sensors Actuator Phys 70(1–2):118–127CrossRefGoogle Scholar
  22. 22.
    Jing Q, Luo H, Mukherjee T, Carley LR, Fedder G (2000) CMOS micromechanical bandpass filter design using a hierarchical MEMS circuit library. Proceedings IEEE 13th Annual International Conference on Micro Electro Mechanical Systems (Miyazaki, Japan, Jan 23–27 2000) pp 187–192Google Scholar
  23. 23.
    Mukherjee T, Zhou Y, Fedder G (1999) Automated optimal synthesis of microaccelerometers. Technical Digest, IEEE International MEMS Conference (Orlando, FL, Jan 17–21, 1999) pp 326–331Google Scholar
  24. 24.
    Deb N, Iyer SV, Mukherjee T, Blanton RD (2001) MEMS resonator synthesis for defect reduction. J Model Simul Microsyst 2(1):11–20Google Scholar
  25. 25.
    Kirkos GA, Jurgilewicz RP, Duncan SJ (1999) MEMS optimization incorporating genetic algorithms. Proceedings of SPIE, no. 3680: Design, Test, and Microfabrication of MEMS and MOEMS (Paris, France, March 1999) pp 84–93Google Scholar
  26. 26.
    Ma L, Antonsson EK (2003) Robust mask-layout and process synthesis. J Microelectromech Syst 12(5):728–739CrossRefGoogle Scholar
  27. 27.
    Ma L, Antonsson EK (2000) Mask-layout and process synthesis for MEMS. MSM’2000, Modeling and Simulation of Microsystems, Semiconductors, Sensors and Actuators (San Diego, CA, April 2000) pp 20–23Google Scholar
  28. 28.
    Zhou N, Zhu B, Agogino AM, Pister KSJ (2001) Evolutionary synthesis of microelectromechanical systems design. Proceedings of the Artificial Neural Networks in Engineering (ANNIE2001) vol. 11 pp 197–202Google Scholar
  29. 29.
    Zhou N, Agogino AM, Pister KS (2002) Automated design synthesis for micro-electro-mechanical systems (MEMS). Proceedings of ASME Design Automation Conference 2002 vol.2 pp 267–273Google Scholar
  30. 30.
    Kamalian R, Agogino AM, Takagi H (2004) The role of constraints and human interaction in evolving MEMS designs: microresonator case study. Proceedings of DETC/DAC Paper # DETC2004-57462, CD ROM, ISBN # I710CD, 2004Google Scholar
  31. 31.
    Kamalian R, Takagi H, Agogino AM (2004) Optimized design of MEMS by evolutionary multi-objective optimization with interactive evolutionary computation. Proceedings of GECCO 2004, Genetic and Evolutionary Computation Conference (Seattle, June 2004) pp 1030–1041Google Scholar
  32. 32.
    Benkhelifa E, Moniri M, Riwari A, de Rueda AG (2011) Evolutionary multi-objective design optimization of energy harvesting MEMS: the case of a piezoelectric. 2011 IEEE Congress on Evolutionary Computation pp 1856–1863Google Scholar
  33. 33.
    Zhang Y, Kamalian R, Agogino AM, Séquin CH (2005) Hierarchical MEMS synthesis and optimization. Smart Structures and Materials 2005: Smart Electronics, MEMS, BioMEMS, and Nanotechnology, Proceedings of SPIE vol. 5763 pp 96–106Google Scholar
  34. 34.
    Cobb CL, Zhang Y, Agogino AM (2006) MEMS design synthesis: integrating case-based reasoning and multi-objective genetic algorithms. Proc. of 2006 SPIE Smart Materials, Nano-and Micro-Smart Systems, SPIE vol. 6414 #641419Google Scholar
  35. 35.
    Cobb CL, Agogino AM (2010) Case-based reasoning for evolutionary MEMS design. J Comput Inf Sci Eng 10(3):031005CrossRefGoogle Scholar
  36. 36.
    Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArborGoogle Scholar
  37. 37.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison, New YorkMATHGoogle Scholar
  38. 38.
    Zhang Y, Agogino AM (2011) Hierarchical component-based representations for evolving microelectromechanical systems designs. AI EDAM 25(1):41–55MATHGoogle Scholar
  39. 39.
    Samuels H (1996) Single- and dual-axis micromachined accelerometers. Analog-Dialogue 30(4):1996Google Scholar
  40. 40.
    Zhang Y (2006) MEMS design synthesis based on hybrid evolutionary computation. Dissertation, University of California at BerkeleyGoogle Scholar
  41. 41.
    Zhang Y, Kamalian R, Agogino AM, Séquin CH (2006) Design synthesis of microelectromechanical systems using genetic algorithms with component-based genotype representation. Proceedings of GECCO (Genetic and Evolutionary Computation Conference) vol.1 pp 731–738Google Scholar
  42. 42.
    Senturia SD (2001) Microsystem design. Kluwer, BostonGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Mechanical EngineeringUniversity of CaliforniaBerkeleyUSA

Personalised recommendations