Multiobjective FET modeling using particle swarm optimization based on scattering parameters with Pareto optimal analysis

In this paper, design-oriented field effect transistor (FET) models are produced. For this purpose, FET modeling is put forward as a constrained, multiobjective optimization problem. Two novel methods for multiobjective optimization are employed: particle swarm optimization (PSO) uses the single-objective function, which gathers all of the objectives as aggregating functions; and the nondominated sorting genetic algorithm-II (NSGA-II) sorts all of the trade-off solutions on the Pareto frontiers. The PSO solution is compared with the Pareto optimum solutions in the biobjective plane and the success of the first method is verified. Furthermore, the resulting FET models are compared with similar FET models from the literature, and thus a comparative study is put forward with respect to the success of the optimization algorithms and the performances and utilizations of the models in the amplification circuits.

Anahtar Kelimeler:

FET modeling, scattering parameters, stability, particle swarm optimization, pareto optimality

Multiobjective FET modeling using particle swarm optimization based on scattering parameters with Pareto optimal analysis

Keywords:

FET modeling, scattering parameters, stability, particle swarm optimization, pareto optimality,

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(12.b). From Figure 8, one can observe that the solution resulting from the PSO process takes place within a reasonably good region of the Pareto front in view of the objective values.
Thus, in the upper region of the Pareto front with respect to the PSO solution point (circle), the gain is decreasing, while the other scattering performances, including losses, get increasingly better. In the lower region, from the counter to upper region, nondominated solutions have increasing gain performances but worsening loss performances. 5. Conclusions
FET modeling, subject to optimum scattering performances, was put forward as a constrained multiobjective optimization problem. In this problem, the elements of the complete FET model were examined to satisfy all expected performance requirements of an active device, including the maximum transducer power gain (GT⇔ S21) , minimum input reﬂection (S
K. Kurokawa, “Power waves and the scattering matrix”, IEEE Transactions on Microwave Theory and Techniques, Vol. 3, pp. 194-202, 1965.
H. Fukui, “Available power gain, noise Şgure and noise measure of two-ports and their graphical representations”, IEEE Transactions on Circuit Theory, Vol. 13, pp. 137-142, 1966.
D. Woods, “Reappraisal of the unconditional stability criteria for active 2-port networks in terms of S parameters”, IEEE Transactions on Circuits and Systems, Vol. 23, pp. 73-81, 1976.
A.M. Bj¨orn, “A graphic design method for matched low-noise ampliŞers”, IEEE Transactions on Microwave Theory and Techniques, Vol. 38, pp. 118-122, 1990.
M.L. Edwards, J.H. Sinsky, “A new criterion for linear 2-port stability using a single geometrically derived param- eter”, IEEE Transactions on Microwave Theory and Techniques, Vol. 40, pp. 2303-2311, 1992.
F. G¨une¸s, M. G¨une¸s, M. Fidan, “Performance characterisation of a microwave transistor”, IEE Proceedings Circuits, Devices & Systems, Vol. 141, pp. 337-344, 1994.
F. G¨une¸s, B.A. C¸ etiner, “A novel Smith chart formulation of performance characterisation for a microwave transis- tor”, IEE Proceedings Circuits, Devices & Systems, Vol. 145, pp. 419-428, 1998.
F. G¨une¸s, C. Tepe, “Gain-bandwidth limitations of microwave transistor”, International Journal of RF and Mi- crowave CAE, Vol. 12, pp. 483-495, 2002.
F. G¨une¸s, S. Demirel, “Gain gradients applied to optimization of distributed-parameter matching circuits for a microwave transistor subject to its potential performance,” International Journal of RF and Microwave CAE, Vol. 18, pp. 99-111, 2008.
C. Paoloni, S. D’Agostino, “An approach to distributed ampliŞer based on a design-oriented FET model”, IEEE Transactions on Microwave Theory and Techniques, Vol. 43, pp. 272-277, 1995.
T. G¨unel, “A continuous hybrid approach to the FET modelling for the maximum transducer power gain”, Microwave and Optical Technology Letters, Vol. 35, pp. 348-352, 2002.
C. Paoloni, “A simpliŞed procedure to calculate the power gain deŞnitions of FET’s,” IEEE Transactions on Microwave Theory and Techniques, Vol. 48, pp. 470-474, 2002.
J. Kennedy, R.C. Eberhart, “Particle swarm optimization”, in Proceedings IEEE Conference on Neural Networks, Vol. 4, pp. 1942-1948, 1995.
K. Deb, A. Pratap, S. Agrawal, T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol. 6, pp. 182-197, 2002.
K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, Chichester, John Wiley & Sons, 2004.