Optimization algorithm of neural network on RF MEMS switch for wireless and mobile reconfigurable antenna applications

Chawla, Paras; Khanna, Rajesh

doi:10.1109/pdgc.2012.6449913

Cited by 9 publications

(10 citation statements)

References 12 publications

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“…The cost function relates the variable values to field quantities and design parameters. The optimizer can then maximize or minimize the value of the design parameter by varying the problem variables [21][22][23]. The problem statement and objective function is by varying the width dimension of spiral antenna to find the return loss from wave port 1 to wave port 1 and therefore defining the cost function to be "−mag (S (WavePort1, WavePort1) −10 dB)" at some specific range of frequencies.…”

Section: Optimization Of Antenna Using Quasi Newton Methodsmentioning

confidence: 99%

A new spiral frequency reconfigurable antenna with RF-MEMS switches for mobile RF front end

Chawla

Khanna

2015

International Journal of Applied Electromagnetics and Mechanics

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The full-wave analyses and design of a multiband reconfigurable antenna (MRA) using radio frequency microelectromechanical system (RF MEMS) switches is presented for 1.0-10.0 GHz band. Integration of spiral antenna and MEMS switches on a conventional FR4 printed circuit board (εr = 4.4 and tan δ = 0.002) is designed. This paper also gives the construction and performance of low loss MEMS resistive switches and further describes the important improvements in the construction of the switches. The RF switches used here exhibit very good RF characteristics with typical values of pull-in voltages lies between 3.5 to 20.25 V. The cantilever beam structure of Design-3 switch offers pull-in voltage as low as 3.5 V at air gap g0 = 0.5 μm. Further, Design-1 switch have insertion losses lower than 0.39 dB in ON position, and isolation of "Design-2" switch as high as 79.83 dB in OFF position. As compared to all presented switches the "Design-3" switch has optimized displacement time response i.e. 2 ms. The effect of optimized "Design-3" switch is than study along with spiral antenna. The overall arm length of spiral is changed by activating and deactivating the three "Design-3" switches and consequently its frequency reconfigured. Finally, optimization procedure using quasi Newton method is introduced for characterizing antenna by changing slot length. The wide bandwidth ranging from 200 MHz to 1.65 GHz at different multiband of proposed MRA indicates device contribution in enhancing the mobile TV and internet application speed.

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Section: Optimization Of Antenna Using Quasi Newton Methodsmentioning

confidence: 99%

A new spiral frequency reconfigurable antenna with RF-MEMS switches for mobile RF front end

Chawla

Khanna

2015

International Journal of Applied Electromagnetics and Mechanics

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“…Recently, ANN models have been widely used to simulate electrical or mechanical characteristics of different MEMS devices. They have been applied mostly to the models of pull-in voltage [20,21,22] and S parameter [23,24,25,26,27,28] of RF-MEMS (radio frequency MEMS) switches, to resonant frequency [29] and spurious modes [30] of RF-MEMS resonator, and to S parameter [31,32,33] of RF-MEMS phase shifter. Uncertainty analysis is the key technique and research focus in the MEMS optimization.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of sensitivity of MEMS microphone based on artificial neural network

Liu

Jia

2019

IEICE Electron. Express

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Due to the uncertainties from manufacturing processes and material properties, MEMS (micro-electro-mechanical system) microphone may exhibit significant variations in their performance compared to the nominal design. The published analytical methods have obvious shortcoming, which cannot meet the needs of both accuracy and efficiency. In order to improve efficiency, the MC (Monte Carlo) simulation based on ANN (artificial neural network) is presented to analyze the uncertainty of sensitivity of polysilicon circular clamped diaphragm microphone. Using the PDS (probabilistic design system) of Ansys software and MC simulation to predict the qualified rate of microphone, the simulated results are 91.2% and 91.4% respectively. The qualified rate of manufactured microphones is 91.5%. In addition, the time-consuming of these two simulations are 10 minutes and about 3000 minutes. This paper also analyzes the change of sensitivity probability densities with the varieties of nominal parameters of diaphragm. The results show that the presented MC simulation with accuracy and high efficiency is an alternative to the traditional methods.

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“…These neurons are simple and many, contain nonlinear types of functional blocks, and are mutually connected by very similar synaptic weights. During the learning process, these synaptic weights could be weakened or strengthened and therefore help the data to be kept in the ANN [5,6]. The benefits, feasibility, and flexibility of ANNs have no formula necessary to design a planar antenna due to the empirical nature, based on the observation of physical phenomena, less computational time as compared with other optimization methods, and compatibility with commercial electromagnetics software [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A novel approach of design and analysis of fractal antenna using a neurocomputational method for reconfigurable RF MEMS antenna

Chawla¹,

Khanna²

2016

Turk J Elec Eng & Comp Sci

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Abstract:A mathematical neural approach/artificial neural network (ANN) for the design of a swastika-shaped reconfigurable antenna as a feedforward side is proposed. Further design parameter calculations using the reverse procedure of the above method is presented. Neural network computational is one of the optimization methods that could be considered to improve the performance of the device. In this paper, the proposed planar antenna up to the 2nd iteration is simulated using finite element method-based HFSS software. The developed ANN algorithm method allows the optimization of the antenna to be carried out by exchanging repetitive simulations and also provides reduced processing times while still retaining great accuracy as compared to traditional mathematical formulation. The simulated S-parameter (return loss) results of the proposed antenna are verified with the ANN and show good agreement. Furthermore, for proof of concept, the above proposed antenna as well as a swastika-shaped reconfigurable antenna (2nd iteration) with radio frequency microelectromechanical system switches are fabricated and tested using a vector network analyzer. The results presented here show that the antenna works well in the frequency range of 1.5 to 6.5 GHz and resonates on multiple bands. The novelty of the approach described here offers ease of designing the process using the ANN algorithm, maintaining the miniaturization of antenna size, multiband behavior, and utility of the antenna in the mobile terminal.

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Optimization algorithm of neural network on RF MEMS switch for wireless and mobile reconfigurable antenna applications

Cited by 9 publications

References 12 publications

A new spiral frequency reconfigurable antenna with RF-MEMS switches for mobile RF front end

A new spiral frequency reconfigurable antenna with RF-MEMS switches for mobile RF front end

Uncertainty analysis of sensitivity of MEMS microphone based on artificial neural network

A novel approach of design and analysis of fractal antenna using a neurocomputational method for reconfigurable RF MEMS antenna

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