Design of an on-chip Hilbert fractal inductor using an improved feed forward neural network for Si RFICs

Authors : Akhendra Kumar PADAVALA, Bheema Rao NISTALA

Pages : 2437-2447

View : 15 | Download : 8

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This paper presents an efficient modeling of Hilbert fractal inductors by improved feed forward neural network trained hybrid particle swarm optimization and gravitational search algorithm insert ignore into journalissuearticles values(FNNPSOGSA);. The proposed model computes the effective inductance value insert ignore into journalissuearticles values(L); and quality factor insert ignore into journalissuearticles values(Q); of Hilbert fractal inductors with metal trace width, effective fractal length, frequency, and oxide thickness as input parameters. In contrast to the traditional feed forward neural network, the proposed FNNPSOGSA has been designed with fewer hidden neurons with much-enhanced learning and generalization capabilities. As a consequence, the proposed model achieves better speed and is as accurate as electromagnetic simulations. From the simulation results, it is proved that the proposed model is a good alternative for complex fractal inductor design.
Keywords : Hybrid particle swarm optimization and gravitational search algorithm PSOGSA, high frequency structural simulator HFSS, inductance value insert ignore into jou