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On modeling of substrate loading in GaN HEMT using grey wolf algorithm

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Abstract

In this paper, four different equivalent circuit models to describe substrate loading effect in GaN HEMT on Si substrate are investigated. The effect is characterized by Z-parameter measurements of open de-embedding structure for 16 × 200-μm GaN HEMT on Si substrate. The grey wolf optimization (GWO)-based procedure is developed to extract optimal values for the model elements. The performance of the proposed technique is evaluated by using two other meta-heuristic optimizations, the well-known particle swarm and the recently developed whale algorithm. The three extraction procedures are evaluated in terms of their effectiveness and rate of convergences. The models are validated by means of S-parameters simulation for the considered device at different passive and active bias conditions. A very good agreement with measurements is achieved when using the GWO, validating its applicability for small- and large-signal modeling applications.

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References

  1. Jarndal, A., Essaadali, R., Kouki, A.: A reliable model parameter extraction method applied to AlGaN/GaN HEMTs. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 35(2), 211–219 (2016)

    Article  Google Scholar 

  2. Hussein, A., Jarndal, A.: An improved reliable PSO based parameter extraction method applied to GaN HEMTs for mm-wave applications. In: International conference on electrical and computing technologies and applications (ICECTA), Ras Al Khaimah, p. 1 (2017)

  3. Chumbes, E., Schremer, A., Smart, J., Wang, Y., MacDonald, N., Hogue, D., Komiak, J., Lichwalla, S., Leoni, R., Shealy, J.: AlGaN/GaN high electron mobility transistors on Si(111) substrates. IEEE Trans. Electron. Dev. 48, 420–426 (2001)

    Article  Google Scholar 

  4. Johnson, J.W., Gao, J., Lucht, K., Williamson, J., Strautin, C., Riddle, J., Therrien, R., Rajagopal, P., Roberts, J.C., Vescan, A., Brown, J.D., Hanson, A., Singhal, S., Borges, R., Piner, E.L., Linthicum, K.J.: Material process and device development of GaN-based HFETs on silicon substrates. Proc. Electrochem. Soc. 6, 405–419 (2004)

    Google Scholar 

  5. Jarndal, A., Kompa, G.: A simple, direct and reliable extraction method applied to GaN devices. Int. J. Electron. (2016). https://doi.org/10.1080/00207217.2016.1218058

    Article  Google Scholar 

  6. Hussein, A., Jarndal, A.: Particle-swarm based small-signal modeling applied to GaN devices. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37(9), 1816–1824 (2018)

    Article  Google Scholar 

  7. Jarndal, A., Hussein, A., Crupi, G., Caddemi, A.: Reliable noise modeling of GaN HEMTs for designing low-noise amplifiers. Int. J. Numer. Model. (2019). https://doi.org/10.1002/jnm.2585.

    Article  Google Scholar 

  8. Sahoo, A.K., Subramani, N.K., Nallatamby, J.C., Sylvain, L., Loyez, C., Quere, R., Medjdoub, F.: Small signal modeling of high electron mobility transistors on siliconand silicon carbide substrate with consideration of substrate lossmechanism. Solid State Electron. 115(Part A), 12–16 (2016)

    Article  Google Scholar 

  9. Rehman, S., Rafique, U., Ahmed, U., Khan, M., Ahmed, M.: Effects of substrate on the AC performance of submicron GaN HEMTs. In: Proceedings of 13th International Conference on Emerging Technologies (ICET), Islamabad, 2017

  10. Alt, A., Marti, D., Bolognesi, C.: Transistor Modeling. IEEE Microwave Magazine, Piscataway (2013)

    Google Scholar 

  11. Chumbes, E., Schremer, A., Smart, J., Wang, Y., Mac Donald, N., Hogue, D., Komiak, J., Lichwalla, S., Leoni, R., Shealy, J.: AlGaN/GaN high electron mobility transistors on Si(111) substrates. IEEE Trans. Electron. Dev. 48(3), 420–426 (2001)

    Article  Google Scholar 

  12. Mirjalili, S., Mirjalili, S., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  13. Gu, Q., Li, X., Jiang, S.: Hybrid genetic grey wolf algorithm for large-scale global optimization. Complexity, vol. 2019, Article ID 2653512, p. 18

  14. Komaki, G.M., Kayvanfar, V.: Grey wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time. J. Comput. Sci. 8, 109–120 (2015)

    Article  Google Scholar 

  15. Daniel, E., Anitha, J., Kamaleshwaran, K.K., Rani, I.: Optimum spectrum mask based medical image fusion using grey wolf optimization. Biomed. Signal Process. Control 34, 36–43 (2017)

    Article  Google Scholar 

  16. Kennedy, J., Eberhart,R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

  17. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  18. Bozorg-Haddad, O., Solgi, M., Loáiciga, Hugo A.: Metaheuristic and Evolutionary Algorithms for Engineering Optimization. Wiley, New York (2017)

    Book  Google Scholar 

  19. Bonyadi, M.R., Michalewicz, Z.: Analysis of stability, local convergence, and transformation sensitivity of a variant of the particle swarm optimization algorithm. IEEE Trans. Evol. Comput. 20(3), 370–385 (2016)

    Article  Google Scholar 

  20. Tawhid, M.A., Ali, A.F.: A hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function. Memet. Comput. 9, 1–13 (2017)

    Article  Google Scholar 

  21. Mohammed, H.M., Umar, S.U., Rashid, T.A.: A systematic and meta-analysis survey of whale optimization algorithm. Comput. Intell. Neurosci., vol. 2019, Article ID 8718571, p. 25

  22. Crupi, G., Schreurs, D.: Microwave de-embedding. Academic Press, Oxford (2014)

    Google Scholar 

  23. Jarndal, A.H., Muhaureq, S.: A particle swarm neural networks electrothermal modeling approach applied to GaN HEMTs. J. Comput. Electron. 18, 1272–1279 (2019). https://doi.org/10.1007/s10825-019-01397-1

    Article  Google Scholar 

  24. Faris, H., Aljarah, I., Al-Betar, M., Mirjalili, S.: Grey wolf optimizer: a review of recent variants and applications. Neural Comput. Appl. 30, 413–435 (2018)

    Article  Google Scholar 

  25. Gao, Z.-M., Zhao, J.: An improved grey wolf optimization algorithm with variable weights. Comput. Intell. Neurosci. vol. 2019, Article ID 2981282, p. 13

  26. Rodríguez, L., Castillo, O., Soria, J.: Grey wolf optimizer with dynamic adaptation of parameters using fuzzy logic. In: 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 3116–3123 (2016)

  27. Fan, J., Hu, M., Chu, X., Yang, D.: A comparison analysis of swarm intelligence algorithms for robot swarm learning. In: 2017 Winter Simulation Conference (WSC), Las Vegas, NV, Dec. 2017

  28. Md Rahman Tanveer, S., Md Islam, J., Akhand, M.A.H.: A comparative study on prominent swarm intelligence methods for function optimization. Glob. J. Technol. Optim. 7, 203 (2016). https://doi.org/10.4172/2229-8711.1000203

    Article  Google Scholar 

  29. vanden Bergh, F., Engelbrecht, P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)

    Article  Google Scholar 

  30. Jarndal, A., Markos, A.Z., Kompa, G.: Improved modeling of GaN HEMT on Si substrate for design of RF power amplifiers. IEEE Trans. Microw. Theory Techn. 59(3), 644–651 (2011)

    Article  Google Scholar 

  31. Jarndal, A.: Measurements uncertainty and modeling reliability of GaN HEMTs. In: The International Conference on Modeling, Simulation and Applied Optimization conference, Tunisia, Hammamet, pp. 1–4, April 2013

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The author gratefully acknowledges the support from the University of Sharjah, Sharjah, United Arab Emirates.

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Correspondence to Anwar Jarndal.

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Jarndal, A. On modeling of substrate loading in GaN HEMT using grey wolf algorithm. J Comput Electron 19, 576–590 (2020). https://doi.org/10.1007/s10825-020-01464-y

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