Skip to main content

Advertisement

SpringerLink
Log in

Evolutionary design optimization of MEMS: a review of its history and state-of-the-art

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

As an important part of the internet of things (IoTs) and cyber-physical systems (CPS), Micro-Electro-Mechanical-Systems (MEMS) is playing more and more irreplaceable role in current industrial community and the forthcoming era of the Industry 4.0. The limitations of some frequently used design methods for MEMS design optimization are analyzed in this review. In order to overcome these difficulties, a recent trend in design optimization of MEMS is inspired by the natural evolution mechanism. Many powerful techniques, especially the evolutionary computation (EC), have been used for the design optimization of MEMS. This paper presents a review of the achievements in this promising research area which utilizes EC methods for the design optimization of MEMS and also proposes three open issues that it is facing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Uhlmann, E., Laghmouchi, A., Ehrenpfordt, R., Hohwieler, E., Geisert, C.: Intelligent electronic system for condition monitoring in Industrie 4.0—micro electromechanical electronic system for condition, process and system monitoring. Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb 111(12), 855–857 (2016)

    Article  Google Scholar 

  2. Zhou, N., Zhu, B., Agogino, A.M., Pister, K.S.J.: Evolutionary synthesis of MEMS MicroElectronicMechanical systems design. In: Intelligent Engineering System through Artificial Neural Networks Artificial Neural Networks in Engineering (ANNIE2001), vol. 11 (ASME Press, New York, 2001), pp. 197–202

  3. Kamalian, R., Zhou, N., Agogino, A. M.: A comparison of MEMS synthesis techniques. In: Proceedings of Pacific Rim Workshop on Transducers & Micro/nano Technologies, pp. 22–24 (2002)

  4. Zhou, N., Agogino, A. M., Pister, K. S. J.: Automated design synthesis for micro-electro-mechanical systems (MEMS). In: Proceedings of the ASME Design Engineering Technical Conference, pp. 68–73 (2002)

  5. Zhang, Y., Agogino, A.M.: Hierarchical component-based representations for evolving microelectromechanical systems designs. Artif. Intell. Eng. Des. Anal. Manuf. 25(1), 41–55 (2011)

    Article  Google Scholar 

  6. Kamalian, R., Takagi, H., and Agogino, A. M.: Optimized design of MEMS by evolutionary multi-objective optimization with interactive evolutionary computation. In: Genetic and Evolutionary Computation—GECCO 2004, Genetic and Evolutionary Computation Conference, Seattle, WA, USA, Proceedings, vol. 3103, pp. 1030–1041. DBLP 26–30 June 2004

  7. Cobb, C. L., Zhang, Y., Agogino, A. M.: MEMS design synthesis: integrating case-based reasoning and multi-objective genetic algorithms. In: Smart Materials, Nano- and Micro-Smart Systems, vol. 6414, pp. 641419–641419–11. International Society for Optics and Photonics (2006)

  8. Cobb, C.L., Agogino, A.M.: Case-based reasoning for evolutionary MEMS design. J. Comput. Inf. Sci. Eng. 10(3), 152–164 (2010)

    Article  Google Scholar 

  9. Zhang, Y., Agogino, A. M.: Interactive hybrid evolutionary computation for MEMS design synthesis. Advances in Neural Network Research and Applications (Springer, Berlin, 2010), pp. 32–38

  10. Fan, Z., Wang, J., Achiche, S., Goodman, E., Rosenberg, R.: Structured synthesis of MEMS using evolutionary approaches. Appl. Soft Comput. 8(1), 579–589 (2008)

    Article  Google Scholar 

  11. Fan, Z., Goodman, E. D., Wang, J., Rosenberg, R.: Hierarchical evolutionary synthesis of MEMS. In: Evolutionary Computation, 2004. CEC2004. Congress on vol. 2, pp. 2320–2327. IEEE (2004)

  12. Wang, J., Fan, Z., Terpenny, J.P., Goodman, E.D.: Knowledge interaction with genetic programming in mechatronic systems design using bond graphs. IEEE Trans. Syst. Man Cybern. Part C 35(2), 172–182 (2005)

    Article  Google Scholar 

  13. Agrawal, D., Seshia, A.: An analytical formulation for phase noise in MEMS oscillators. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 61(12), 1938–1952 (2014)

    Article  Google Scholar 

  14. Robertson, B., Yang, H., Redmond, M.M., Collings, N., Moore, J.R., Liu, J., et al.: Demonstration of multi-casting in a 1 × 9 LCOS wavelength selective switch. J. Lightwave Technol. 32(3), 402–410 (2013)

    Article  Google Scholar 

  15. Li, H., Antonsson, E. K.: Evolutionary techniques in MEMS synthesis. In: Proceedings DETC’98, 1998 ASME Design Engineering Technical Conferences, Atlanta, GA, pp. 1–5 (1998)

  16. Fan, Z., Wang, J., Wen, M., Goodman, E., Rosenberg, R.: An evolutionary approach for robust layout synthesis of MEMS. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Proceedings, vol. 51, pp. 1186–1191. IEEE (2005)

  17. Senturia, S.D.: Microsystem Design. Springer, New York (2001)

    Google Scholar 

  18. Fan, Z., Seo, K., Hu, J., Rosenberg, R. C., Goodman, E. D.: System-level synthesis of MEMS via genetic programming and bond graphs. In: Genetic and Evolutionary Computation—GECCO 2003, Genetic and Evolutionary Computation Conference, Chicago, IL, USA, Proceedings, vol. 2724, pp. 2058–2071. DBLP, 12–16 July 2003

  19. Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection. MlT Press, Cambridge (1992)

    MATH  Google Scholar 

  20. Wang, Z.S.: Bond Graph Theory and Its Application in System Dynamics. Harbin Engineering University Press, Harbin (2007). [In Chinese]

    Google Scholar 

  21. Lin, D., Miller, T.: Robust MEMS gyroscope for oil and gas exploration. In: SPIE Sensing Technology + Applications, vol. 9113, pp. 91130I (2014)

  22. Ma L., Antonsson E. K.: Automated mask-layout and process synthesis for MEMS. In: Technical Proceedings of the 2000 International Conference on Modeling and Simulation of Microsystems, pp. 20–23 (2000)

  23. Li, H., Antonsson, E. K.: Mask-layout synthesis through an evolutionary algorithm. In: Msm’99 Modeling & Simulation of Microsystems Semiconductors Sensors & Actuators, 1999 (2013)

  24. Toshiyoshi, H., Konishi, T., Machida, K., Masu, K.: A mixed-design technique for integrated MEMS using a circuit simulator with HDL. In: Mixed Design of Integrated Circuits and Systems pp. 17–22. IEEE (2013)

  25. Konishi, T., Machida, K., Maruyama, S., Mita, M., Masu, K., Toshiyoshi, H.: A single-platform simulation and design technique for CMOS-MEMS based on a circuit simulator with hardware description language. J. Microelectromech. Syst. 22(3), 755–767 (2013)

    Article  Google Scholar 

  26. Farnsworth, M., Benkhelifa, E., Tiwari, A., Zhu, M., Moniri, M.: An efficient evolutionary multi-objective framework for MEMS design optimisation: validation. Compar. Anal. Memet. Comput. 3(3), 175–197 (2011)

    Article  Google Scholar 

  27. Gong, M.G., Jiao, L.C., Yang, D.D., Ma, W.P.: Research on evolutionary multi-objective optimization algorithms. J. Softw. 20(2), 271–289 (2009). [In Chinese]

    Article  MathSciNet  Google Scholar 

  28. SUGAR, Simulation research for MEMS, http://wwwbsac.eecs.berkeley.edu/cadtools/sugar/sugar/

  29. Kamalian, R.: Evolutionary synthesis of MEMS devices. Ph.D. Dissertation, University of California at Berkeley (2004)

  30. Zhang, Y.: MEMS design synthesis based on hybrid evolutionary computation. Ph.D. Dissertation, University of California at Berkeley (2006)

  31. Michalewicz, Z., Fogel, D.: How to Solve Problems-modern Heuristic Method. China WaterPower Press, Beijing (2003). [In Chinese]

    MATH  Google Scholar 

  32. Lohn, J. D., Kraus, W. F., Hornby, G. S.: Automated design of a MEMS resonator. In: IEEE Congress on Evolutionary Computation, pp. 3486–3491. IEEE (2007)

  33. Tutum, C. C., Fan, Z.: Multi-criteria layout synthesis of MEMS devices using memetic computing. In: IEEE Congress of Evolutionary Computation, vol. 81, pp. 902–908. IEEE (2011)

  34. Benkhelifa, E., Moniri, M., Tiwari, A., Rueda, A. G. D.:Evolutionary multi-objective design optimisation of energy harvesting MEMS: the case of a piezoelectric. In: IEEE Congress of Evolutionary Computation, vol. 47, pp. 1856–1863. IEEE (2011)

  35. Belfiore, N. P., Emamimeibodi, M., Verotti, M., Crescenzi, R.: Kinetostatic optimization of a MEMS-based compliant 3 DOF plane parallel platform. In: IEEE, International Conference on Computational Cybernetics, pp. 26–266. IEEE (2013)

  36. Liu, Y., Yang, H.Y., Wang, G.C.: Genetic algorithm based multidisciplinary design optimization of MEMS accelerometer. Appl. Mech. Mater. 101–102, 530–533 (2011)

    Article  Google Scholar 

  37. Chen, F., Chang, H., Yuan, W., Wilcock, R., Kraft, M.: Parameter optimization for a high-order band-pass continuous-time sigma-delta modulator MEMS gyroscope using a genetic algorithm approach. J. Micromech. Microeng. 22(10), 105006 (2012)

    Article  Google Scholar 

  38. Lin, M., Antonsson, E. K.: Robust mask-layout synthesis for MEMS. In: Technical Proceedings of the International Conferecne on Modeling & Simulation of Microsystems Chapter Optimization Nanotech, pp. 128–131 (2001)

  39. Hornby, G. S., Kraus, W. F., Lohn, J. D.: Evolving MEMS resonator designs for fabrication. In: International Conference on Evolvable Systems: From Biology to Hardware, vol. 5216 (Springer, New York, 2008), pp. 213–224

  40. Deb, K.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2000)

    Article  Google Scholar 

  41. Fan, Z., Liu, J., Sorensen, T., Wang, P.: Improved differential evolution based on stochastic ranking for robust layout synthesis of MEMS components. IEEE Trans. Industr. Electron. 56(4), 937–948 (2009)

    Article  Google Scholar 

  42. Jang, G.W.: Topology optimization of MEMS considering etching uncertainties using the level-set method. Int. J. Numer. Methods Eng. 92(6), 571–588 (2012)

    Article  MathSciNet  Google Scholar 

  43. Martowicz, A., Uhl, T.: Reliability- and performance-based robust design optimization of MEMS structures considering technological uncertainties. Mech. Syst. Sig. Process. 32, 44–58 (2012)

    Article  Google Scholar 

  44. Liu, X.M., Li, H., Xiong, M.Z.: Parallel evolutionary algorithm for MEMS relay parameters optimization. Comput. Eng. Appl. 2014(6), 200–204 (2014). [In Chinese]

    Google Scholar 

  45. Chaudhuri, S., Deb, K.: An interactive evolutionary multi-objective optimization and decision making procedure. Appl. Soft Comput. J. 10(2), 496–511 (2010)

    Article  Google Scholar 

  46. Dong, N., Wang, Y.P.: Multi-objective evolutionary algorithm based on preference for constrained optimization problems. J. Xidian Univ. 41(1), 98–104 (2014). [In Chinese]

    MathSciNet  Google Scholar 

  47. Zheng, J.H., Xie, C.Z.: A study on how to use angle information to include decision maker’s preferences. Acta Electronica Sinica. 2014(11), 2239–2246 (2014). [In Chinese]

    Google Scholar 

  48. Gong, D.W., Wang, G.X., Sun, X.Y.: Set-based evolutionary optimization algorithms integrating decision-maker’s preferences for many-objective optimization problems. Acta Electronica Sinica. 2014(5), 933–939 (2014). [In Chinese]

    Google Scholar 

  49. Zheng, J.H., Jia, Y.: A dynamic guided multi-objective optimization strategy based on preference information. Patt. Recogn. Artif. Intell. 3, 272–280 (2014). [In Chinese]

    Google Scholar 

  50. Dai, R., Li, y, Li, Q.: Social Intelligence and Metasynthetic System. Posts and Telecommunications Press, Beijing (2013). [In Chinese]

    Google Scholar 

  51. Zhu, H., Wang, P., Fan, Z.: Evolutionary design optimization of MEMS: a brief review. In: IEEE International Conference on Industrial Technology, pp. 1683–1687. IEEE (2010)

  52. Lu, Q., Wang, P., Guo, S., Sheng, B., Liu, X., Fan, Z.: Application of an evolutionary algorithm in the optimal design of micro-sensor. Bio-Med. Mater. Eng. 26(s1), S1711–S1719 (2015)

    Article  Google Scholar 

  53. Liu, J., Fan, Z., Soerensen, T., Xu, Z., Wang, P.: Robust layout synthesis of MEMS using constrained adaptive differential evolution algorithm. Soft Computing: New Research. (Nova Scientific Publisher, New York, 2009)

  54. Wang, P., Zhang, J., Xu, L., Wang, H., Feng, S., Zhu, H.: How to measure adaptation complexity in evolvable systems—a new synthetic approach of constructing fitness functions. Expert Syst. Appl. 38(8), 10414–10419 (2011)

    Article  Google Scholar 

  55. Wang, P., Zhu, H., Wilamowska-Korsak, M., Bi, Z., Li, L.: Determination of weights for multiobjective decision making or machine learning. IEEE Syst. J. 8(1), 63–72 (2014)

    Article  Google Scholar 

  56. Wang, P., Zhang, J., Yang, Y., Peng, Y.Y., Lu, Q.B.: Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions. Science Press, Beijing (2015). [In Chinese]

    Google Scholar 

  57. Wang, P., et al.: Research on Soft Computing Methods in Optimization and Control, 2nd edn. Hubei Science and Technology Press, Wuhan (2017). [In Chinese]

    Google Scholar 

  58. Bourgeois, J., Goldstein, S.C.: Distributed intelligent MEMS: progresses and perspectives. IEEE Syst. J. 9(3), 1057–1068 (2015)

    Article  Google Scholar 

  59. Liang, J., Cao, J., Liu, R., Li, T.: Distributed intelligent MEMS: a survey and a real-time programming framework. ACM Comput. Surv. 49(1), 20 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 71371148), the Fundamental Research Funds for the Central Universities (WUT: 2017VIA020).

Author information

Authors and Affiliations

  1. School of Automation, Wuhan University of Technology, Wuhan, China

    Pan Wang

  2. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China

    Qibing Lu

  3. School of Engineering, Shantou University, Shantou, China

    Zhun Fan

Authors
  1. Pan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qibing Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhun Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qibing Lu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, P., Lu, Q. & Fan, Z. Evolutionary design optimization of MEMS: a review of its history and state-of-the-art. Cluster Comput 22 (Suppl 4), 9105–9111 (2019). https://doi.org/10.1007/s10586-018-2085-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2085-3

Keywords

Search

Navigation