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An Optimal Design of a Short-Channel RF Low Noise Amplifier Using a Swarm Intelligence Technique

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Embedded Systems and Artificial Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1076))

Abstract

In this paper, an optimal sizing of a cascode short-channel CMOS low noise amplifier (LNA) with inductive source degeneration by using the ant colony optimization (ACO) and the artificial bee colony (ABC) techniques is presented. The reason for employing this proposed topology is to provide a high conversion gain and improved noise figure (NF) at the operating frequency of 2.3 GHz. In order to validate the obtained results by ACO and ABC techniques, the advanced design system (ADS) simulator is used for this purpose using 90 nm CMOS technology.

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References

  1. Xiaoyu, J.: Optimization of Short-Channel RF CMOS Low Noise Amplifiers by Geometric Programming. Electrical Engineering Theses. Paper 15 (2012)

    Google Scholar 

  2. Reeves, C.R.: Modern Heuristic Techniques for Combinatorial Problems. Blackwell Scientific Publications, Oxford (1993)

    MATH  Google Scholar 

  3. Glover, F.: Tabu search-part II. ORSA J. Comput. 2(1), 4–32 (1990)

    Article  Google Scholar 

  4. Grimbleby, J.B.: Automatic analogue circuit synthesis using genetic algorithms. IEEE Proc.-Circuits, Dev. Syst. 147(6), 319–323 (2000)

    Article  Google Scholar 

  5. Aarts, E., Lenstra, K.: Local Search in Combinatorial Optimization. Princeton University Press, Princeton (2003)

    Book  Google Scholar 

  6. Chan, F.T.S., Tiwari, M.K.: Swarm Intelligence: Focus on Ant and Particle Swarm Optimization. I-Tech Education and Publishing (2007)

    Google Scholar 

  7. Benhala, B., Pereira, P., Sallem, A. (eds.): Focus on Swarm Intelligence Research and Applications. Computer Science, Technology and Applications series. Nova Science Publishers, ISBN: 978-1-53612-452-1 (2017)

    Google Scholar 

  8. Fakhfakh, M., Cooren, Y., Sallem, A., Loulou, M., Siarry, P.: Analog circuit design optimization through the particle swarm optimization technique. J. Anal. Integr. Circuits Sign. Process. (2010)

    Google Scholar 

  9. Benhala, B.: Sizing of an inverted current conveyors by an enhanced ant colony optimization technique. In: The International Conference on Design of Circuits and Integrated Systems (DCIS 2016), November 23–25, (2016), Granada, Spain

    Google Scholar 

  10. Benhala, B.: An improved ACO algorithm for the analog circuits design optimization. Int. J. Circuits, Syst. Sign. Process. 10, 128–133 ISSN: 1998-4464 (2016)

    Google Scholar 

  11. Benhala, B., Bouyghf, H., Lachhab, A., Bouchikhi, B.: Optimal design of second generation current conveyors by the artificial bee colony technique. In: IEEE International Conference on Intelligent Systems and Computer Vision (ISCV’15), pp. 1–5, March 25–26 (2015), Fez, Morocco

    Google Scholar 

  12. Bouyghf, H., Benhala, B., Raihani, A.: Optimal design of RF CMOS circuits by means of an artificial bee colony technique (Chap. 11). In: Benhala, B., Pereira, P., Sallem, A. (eds.) Focus on Swarm Intelligence Research and Applications, pp. 221–246. NOVA Science Publishers, Inc. ISBN: 978-1-53612-452-1 (2017)

    Google Scholar 

  13. Dorigo, M., DiCaro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life J. 5, 137–172 (1999)

    Article  Google Scholar 

  14. Dac-Nhuong, L., et al.: Optimizing feature selection in video-based recognition using Max–Min Ant System for the online video contextual advertisement user-oriented system. J. Comput. Sci. 21, 361–370 (2017)

    Google Scholar 

  15. Bhateja, V., Tripathi, A., Sharma, A., Le, B.N., Satapathy, S.C., Nguyen, G.N., Le, D.N.: Ant colony optimization based anisotropic diffusion approach for despeckling of SAR images. In: International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, pp. 389–396. Springer, Cham (2016, November)

    Google Scholar 

  16. Jinhui, Y., Xiaohu, S., Maurizio, M., Yanchun, L.: An ant colony optimization method for generalized TSP problem. Prog. Nat. Sci. e18, 1417–1422 (2009)

    Google Scholar 

  17. Hung, K.K., Hu, P.K., Ko, C., Cheng, Y.C.: A physics-based MOSFET noise model for circuit simulators. IEEE Trans. Electronic Devices 37, 1323–1333 (1990)

    Article  Google Scholar 

  18. Andreani, P., Sjoland, H.: Noise optimization of an inductively degenerated CMOS low noise amplifier. IEEE Trans. Circuits Syst. 48, 835–841 (2001)

    Article  Google Scholar 

  19. Hoe David, H.K., Xiaoyu, J.: The Design of Low Noise Amplifiers in Deep Submicron CMOS Process: A Convex Optimization Approach. Hindawi Publishing Corporation VLSI Design, ID 312639 V (2015)

    Google Scholar 

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Correspondence to Soufiane Abi .

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Abi, S., Bouyghf, H., Benhala, B., Raihani, A. (2020). An Optimal Design of a Short-Channel RF Low Noise Amplifier Using a Swarm Intelligence Technique. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_14

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