Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-23T07:52:40.740Z Has data issue: false hasContentIssue false
  • English
  • Français

Hierarchical component-based representations for evolving microelectromechanical systems designs

Published online by Cambridge University Press:  07 October 2010

Ying Zhang  and
Alice M. Agogino
Ying Zhang
Affiliation:
School of Electrical and Computer Engineering, Georgia Institute of Technology, Savannah, Georgia, USA
Alice M. Agogino
Affiliation:
Department of Mechanical Engineering, University of California, Berkeley, Berkeley, California, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper we present a genotype representation method for improving the performance of genetic-algorithm-based optimal design and synthesis of microelectromechanical systems. The genetic algorithm uses a hierarchical component-based genotype representation, which incorporates specific engineering knowledge into the design optimization process. Each microelectromechanical system component is represented by a gene with its own parameters defining its geometry and the way it can be modified from one generation to the next. The object-oriented genotype structures efficiently describe the hierarchical nature typical of engineering designs. They also encode knowledge-based constraints that prevent the genetic algorithm from wasting time exploring inappropriate regions of the search space. The efficiency of the hierarchical component-based genotype representation is demonstrated with surface-micromachined resonator designs.

Keywords

Component-Based Genotype RepresentationMicroelectromechanical System DesignMultiobjective Genetic Algorithm
Type
Articles
Information
AI EDAM , Volume 25 , Issue 1 , February 2011 , pp. 41 - 55
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Clark, J.V., Bindel, D., Zhou, N., Nie, J., Kao, W., Zhu, E., Kuo, A., Pister, K.S.J., Demmel, J., Govindjee, S., Bai, Z., Gu, M., & Agogino, A.M. (2002). Addressing the needs of complex MEMS design. Proc. 15th IEEE Int. MEMS Conf., pp. 204–209. New York: IEEE.CrossRefGoogle Scholar
Cobb, C.L., & Agogino, A.M. (in press). Case-based reasoning for evolutionary design. ASME Journal of Computing and Information Science in Engineering.Google Scholar
Cobb, C.L., Zhang, Y., & Agogino, A.M. (2006). MEMS design synthesis: integrating case-based reasoning and multi-objective genetic algorithms. Proc. 2006 SPIE Smart Materials, Nano- and Micro-Smart Systems, Vol. 6414, No. 641419. New York: SPIE.Google Scholar
Deb, N., Iyer, S.V., Mukherjee, T., & Blanton, R.D. (2001). MEMS resonator synthesis for defect reduction. Journal of Modeling and Simulation of Microsystems 2(1), 11–20.Google Scholar
Eberly, D.H. (2000). 3D Game Engine Design: A Practical Approach to Real-Time Computer Graphics. San Francisco, CA: Morgan Kaufmann.Google Scholar
Eshelman, L.J., & Schaffer, J.D. (1991). Real-coded genetic algorithms and interval-schemata. Proc. 1st Workshop on the Foundations of Genetic Algorithms, pp. 187–202, San Mateo, CA.Google Scholar
Fan, Z., Seo, K., Hu, J., Rosenberg, R., & Goodman, E. (2003). System-level synthesis of MEMS via genetic programming and bond graphs. Proc. Genetic and Evolutionary Computation Conf. (GECCO), pp. 2058–2071.CrossRefGoogle Scholar
Fan, Z., Wang, J., Achiche, S., Goodman, E., & Rosenberg, R. (2008). Structured synthesis of MEMS using evolutionary approaches. Applied Soft Computing Journal 8(1), 579–589.CrossRefGoogle Scholar
Fedder, G., & Mukherjee, T. (1996). Physical design for surface-micromachined MEMS. Proc. 5th ACM/SIGDA Physical Design Workshop, pp. 53–60, Reston, VA.Google Scholar
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. New York: Addison–Wesley.Google Scholar
Graf, S. (2004). GA Building Blocks and Data Structures for MEMS/NEMS Design Automation and Synthesis. Diploma Thesis. RWTH Aachen University.Google Scholar
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.Google Scholar
Hornby, G.S., Kraus, W.F., & Lohn, J.D. (2008). Evolving MEMS resonator designs for fabrication. Proc. Evolvable Systems: From Biology to Hardware: 8th Int. Conf., pp. 213–224.CrossRefGoogle Scholar
Kamalian, R., & Agogino, A.M. (2005). Improving evolutionary MEMS synthesis through fabrication and testing feedback. Proc. IEEE Int. Conf. Systems, Man and Cybernetics, SMC2005, pp. 1908–1913.CrossRefGoogle Scholar
Kamalian, R., Agogino, A.M., & Takagi, H. (2004). The role of constraints and human interaction in evolving MEMS designs: microresonator case study. Proc. DETC/DAC, Paper No. DETC2004-57462 [CD].CrossRefGoogle Scholar
Kirkos, G.A., Jurgilewicz, R.P., & Duncan, S.J. (1999). MEMS optimization incorporating genetic algorithms. Proc. SPIE 3680: Design, Test, and Microfabrication of MEMS and MOEMS, pp. 84–93, Paris, March.Google Scholar
Lee, B., & Saitou, K. (2007). Assembly synthesis with subassembly partitioning for optimal in-process dimensional adjustability. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(1), 31–43.CrossRefGoogle Scholar
Lohn, J.D., Kraus, W.F., & Hornby, G.S. (2007). Automated design of a MEMS resonator. IEEE Congress on Evolutionary Computation, pp. 3486–3491, Singapore.CrossRefGoogle Scholar
Ma, L., & Antonsson, E.K. (2000 a). Mask-layout and process synthesis for MEMS. MSM'2000, Modeling and Simulation of Microsystems, Semiconductors, Sensors and Actuators, San Diego, CA, April.Google Scholar
Ma, L., & Antonsson, E.K. (2000 b). Applying genetic algorithms to MEMS synthesis. ASME Int. Mechanical Engineering Congress and Exposition, Orlando, FL, November.Google Scholar
Ma, L., & Antonsson, E.K. (2003). Robust mask-layout and process synthesis. Journal of Microelectromechanical Systems 12(5), 728–739.Google Scholar
McConaghy, T., Palmers, P., Gielen, G., & Steyaert, M. (2007). Simultaneous multi-topology multi-objective sizing across thousands of analog circuit topologies. Design Automation Conf., pp. 944–947, San Diego, CA, June.Google Scholar
Moore, D.S., & McCabe, G.P. (1999). Introduction to the Practice of Statistics. New York: W.H. Freeman.Google Scholar
Mukherjee, T., & Fedder, G. (1997). Structured design of microelectromechanical systems. Proc. 34th ACM Design Automation Conf., pp. 680–685, Anaheim, CA.CrossRefGoogle Scholar
Mukherjee, T., Iyer, S.V., & Fedder, G. (1998), Optimization-based synthesis of microresonators. Sensors and Actuators A: Physical 70(1–2), 118–127.CrossRefGoogle Scholar
Narayanan, S., & Azarm, S. (1999). On improving multiobjective genetic algorithms for design optimization. Structural Optimization 18, 146–155.CrossRefGoogle Scholar
Peysakhov, M., & Regli, W.C. (2003). Using assembly representations to enable evolutionary design of lego structures. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17(2), 155–168.CrossRefGoogle Scholar
Srinivas, N., & Deb, K. (1995). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221–248.CrossRefGoogle Scholar
Tamaki, H., Kita, H., & Kobayashi, S. (1996). Multi-objective optimization by genetic algorithm: a review. Proc. 1996 IEEE Int. Conf. Evolutionary Computation, pp. 517–522, Nagoya, Japan.CrossRefGoogle Scholar
Zhang, Y., Kamalian, R., Agogino, A.M., & Séquin, C.H. (2005). Hierarchical MEMS synthesis and optimization. Proc. SPIE—Smart Structures and Materials 2005: Smart Electronics, MEMS, BioMEMS, and Nanotechnology, Vol. 5763, pp. 96–106, Paper No. 5763_12 [CD].Google Scholar
Zhou, N., Agogino, A.M., & Pister, K.S. (2002). Automated design synthesis for micro-electro-mechanical systems (MEMS). Proc. ASME Design Automation Conf.CrossRefGoogle Scholar
Zhou, N., Zhu, B., Agogino, A.M., & Pister, K.S.J. (2001). Evolutionary synthesis of microelectromechanical systems design. Proc. Artificial Neural Networks in Engineering (ANNIE2001), pp. 197–202.Google Scholar