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Soft Computing Techniques for the Optimization of SAW Filters: A State-of-the-art Review

Prachi Chaudhary, Priyanka, Manoj Duhan Published in Signal Processing

International Journal of Applied Information Systems
Year of Publication: 2015
© 2015 by IJAIS Journal
10.5120/ijais15-451399
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  1. Priyanka Prachi Chaudhary and Manoj Duhan. Article: Soft Computing Techniques for the Optimization of SAW Filters: A State-of-the-art Review. International Journal of Applied Information Systems 9(4):73-80, July 2015. BibTeX

    @article{key:article,
    	author = "Prachi Chaudhary, Priyanka and Manoj Duhan",
    	title = "Article: Soft Computing Techniques for the Optimization of SAW Filters: A State-of-the-art Review",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 9,
    	number = 4,
    	pages = "73-80",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Surface Acoustic Wave (SAW) devices are an important class of piezoelectric devices, providing frequency control, frequency selection, and signal processing capabilities. The SAW devices, designed to handle complex signal processing functions, can offer considerable cost & size advantage over competing technology. The SAW devices, based on the transduction of acoustic waves, are used as filters, oscillators and transformers, devices. The SAW filters are electromechanical devices commonly used in radio frequency applications. The SAW filters are of 4 types; Linear Resonator and Resonator-Filter Devices, Linear Devices Using Unidirectional IDTs, Linear Devices Using Bidirectional IDTs and Nonlinear Devices. Most of work associated with SAW filters deals with the realization of FIR filters may be quite high, resulting in a large size filter. The realization of IIR filters on SAW devices lead to substantial reduction in filter size. But the introduction of reflective units (for realizing poles lead to complex optimizations issues. Soft Computing Techniques (SCT) are the optimization techniques inspired by the cognitive behavior of human mind. These are fusion of methodologies that were designed to model and enable solutions to real world problems, which are not modeled or too difficult to model, mathematically. SCTs are classified in to four important categories; Evolutionary Computation Techniques (ECT), Fuzzy Logic (FL), Neutral Network (NN) and Machine Learning (ML) [23]. The ECTs are probability-based approaches inspired by biological evolution and/or social evolution. The ECTs are based on mechanics of natural selection and natural genetics, of the likes Genetic algorithms (GAs) and local search have already been used in various forms for solving optimization issues related to SAW filter design. Newer ECTs like Memetic Algirithms (MAs), which blends GAs and local search to take care of both the exploration and exploitation of search space, have also been reported to be used for the optimization of some of the SAW filter design. The main goal of the report in this paper is to look in to the use of SCTs for tackling SAW filter design issues and in that context, probe the future scenario of these techniques for such design issues.

Reference

  1. K. Tagawa, “Evolutionary computation techniques for the optimum design of balanced surface acoustic wave filters,” in Proc. of the IEEE Congress on Evolutionary Computation, 2008, pp. 299-304.
  2. Guilling Huang, Qida Zhao, Luming Zhao, “Optimization of SAW transducer design by probabilistic global search Lausanne,” Proc. in electromagnetic research symposium Hangzhou China, March 2008.
  3. S. N. Sivanandam, S. N. Deepa, Introduction to Genetic Algorithms, Springer-Verlag, Berlin, 2008.
  4. K. Tagawa, “Simulation Modeling and Optimization Technique for Balanced Surface Acoustic Wave Filters,” Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization, Beijing, China, September 15-17, 2007.
  5. K. Tagawa, M. Masuoka, and M. Tsukamoto, “Robust optimum design of SAW filters with the Taguchi method and a memetic algorithm,” in Proc. of the 2005 IEEE Congress on Evolutionary Computation, pp.2146-2153.
  6. J. Meltaus, P. Hämäläinen, V. P. Plessky, and M. M. Salomaa, “Genetic optimization algorithms in the design of coupled SAW filters,” in Proc. of IEEE Ultrasonics Symposium, 2004, pp. 1901-1904.
  7. S. Goto and T. Kawakatsu, “Optimization of the SAW filter design by immune algorithm,” in Proc. of IEEE International Ultrasonics, Ferroelectrics and Frequency Control Joint 50th Anniversary Conference, 2004, pp. 600-603.
  8. K. Tagawa, T. Ohtani, T. Igaki, and S. Seki, “Robust optimization design of SAW filters by using penalty function method,” in Proc. of the IEEE International Conference on Industrial Technology, 2004, pp. 751-756.
  9. K. Tagawa, T. Yamamoto, T. Igaki, and S. Seki, “An imanishian genetic algorithm for the optimum design of surface acoustic wave filter,” in Proc. of Congress on Evolutionary Computation, 2003, pp. 2748-2755.
  10. K. Tagawa, K. Togunaka, H. Haneda, T. Igaki, and S. Seki, “Optimal design of three-IDT type SAW filter using local search,” in Proc. of IEEE 28th Annual Conference of the Industrial Electronics Society, 2002, pp. 2572-2577.
  11. H. Ishibuchi, T. Yoshida, & T. Murata, "Balance between genetic search and local search in hybrid evolutionary multi-criterion optimization algorithms” in Proc. of the genetic and Evolutionary Computation Conference, 2001, pp. 1301-1308.
  12. K. Tagawa, N. Wakabayashi, H. Haneda, & K. Inoue, "An imanishism-based genelic algorithm for sampling various Pareto-optimal solutions: an application to the multi-objective resource division problem,” EIectrical Engineering in Japan, vol. 139 (2), pp. 23-25, 2002.
  13. K. Tagawa, H. Haneda, & K. Mizutani, "An lmanishian genetic algorithm: an application to the module placement problem,” Genetic and Evolutionary Computation Conference, 2002, Late Breaking Papers, pp. 427-434.
  14. Christian Prins, Samir Bouchenoua, “A memetic algorithm solving the VRP, the CARP and general routing problems with nodes, edges and arcs,” LOSI – University of Technology of Troyes, 2003.
  15. V. Prabhu, B. S. Panwar and Priyanka, "Linkage learning genetic algorithm for the design of withdrawal weighted SAW filters," in Proc. of IEEE Ultrasonics Symposium, 2002, pp. 357-360.
  16. Peter Merz, Bernd Freisleben, “Memetic algorithms for the travelling salesman problem,” complex Systems, vol. 13, pp. 297–345, 2001.
  17. K. Y. Hashimoto, Surface Acoustic Wave Devices in Telecommunications: Modelling and Simulation, Springer-Verlag, Berlin, 2000.
  18. C. K. Campbell, Surface Acoustic Wave Devices for Mobile and Wireless Communications, Academic Press, 1998.
  19. J. Franz, C. C. W. Ruppel, F. Seifert and R. Weigel, “Hybrid optimization techniques for the design of SAW filters”, in Proc. of IEEE Ultrasonics Symposium, 1997, pp. 33–36.
  20. E. Aarts and J. K. lenstra, (Eds.), Local Search in Combinatorial 0ptimization, John Wiley & Sons, 1997.
  21. T. Kojima and T. Suzuki, “Fundamental equations of electro-acoustic conversion for an interdigital surface-acoustic-wave transducer by using force factors,” Japanese Journal of Applied Physics Supplement, vol. 31, pp. 194-197, 1992.
  22. C. C. W. Ruppel, A. A. Sachs, and F. J. Seifert, “A review of optimization algorithms for the design of SAW transducers,” in Proc. of IEEE Ultrasonics Symposium, 2002, pp. 73-83.
  23. P. K. Dahiya, “Recent Trends in Evolutionary Computation,” Ph. D. thesis, M. D. University, Rohtak, India, April, 2011.

Keywords

Soft Computing Techniques, SAW Filter