Design and Optimization of Miniaturized Microstrip Patch ...

Citation: Boudjerda, M.; Reddaf, A.;

Kacha, A.; Hamdi-Cherif, K.; Alharbi,

T.E.A.; Alzaidi, M.S.; Alsharef, M.;

Ghoneim, S.S.M. Design and

Optimization of Miniaturized

Microstrip Patch Antennas Using a

Genetic Algorithm. Electronics 2022,

11, 2123. https://doi.org/10.3390/

electronics11142123

Academic Editor: Alejandro

Melcón Alvarez

Received: 12 June 2022

Accepted: 5 July 2022

Published: 6 July 2022

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electronics

Article

Design and Optimization of Miniaturized Microstrip PatchAntennas Using a Genetic AlgorithmMounir Boudjerda 1,2, Abdelmalek Reddaf 1, Abdellah Kacha 2, Khaled Hamdi-Cherif 1, Turki E. A. Alharbi 3 ,Mohammed S. Alzaidi 3 , Mohammad Alsharef 3 and Sherif S. M. Ghoneim 3,*

1 Research Center in Industrial Technologies CRTI, ex CSC, BP 64 Cheraga, Algiers 16014, Algeria;[email protected] (M.B.); [email protected] (A.R.); [email protected] (K.H.-C.)

2 Laboratoire de Physique de Rayonnement et Applications, University of Jijel, Jijel 18000, Algeria;[email protected]

3 Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099,Taif 21944, Saudi Arabia; [email protected] (T.E.A.A.); [email protected] (M.S.A.);[email protected] (M.A.)

* Correspondence: [email protected]

Abstract: The main objective of this work is to propose an approach for improving the performanceof miniaturized microstrip patch antennas (MPAs) that are loaded with a thin film consisting of ahigh relative permittivity material. The method uses a thin film to decrease the antenna’s resonancefrequency while keeping the antenna’s patch dimensions. For the enhancement of the antenna’sperformance with a thin film, the dimensions of the patch of the designed antenna are optimizedutilizing genetic algorithms (GAs). The resonance frequency of the microstrip patch antenna waschanged from 5.8 GHz to 4.0 GHz, and the area of the proposed antenna was minimized by around60%, especially in comparison to a conventional antenna alone without thin film. Most of theperformances of the proposed antenna such as the return loss, bandwidth, and voltage standing waveratio (VSWR) were improved.

Keywords: micro-strip patch antenna; thin films; high permittivity dielectric material; antennaminiaturization; genetic algorithm optimization

1. Introduction

Nowadays, wireless devices are widely employed in a variety of domains, includingtelecommunications, aeronautics, medical, and military. The growing use of these sys-tems has led manufacturers to focus on the improvement of wireless devices. Therefore,microwave circuit technology has shown a considerable development in recent years [1–5].

This evolution became possible after the significant progress in electronics and numericalinformation processing techniques. The connection between these terminals, mobile phones,computers, base stations, and other infrastructures is carried out by electromagnetic waves [6].

The antenna is one of the most essential elements of wireless systems. These elementstransform the electrical signal into electromagnetic signals and radiate these in space andvice versa [7]. The antenna takes up the most space in the communication system chain;thus, increasing the antenna’s total size makes the implementation of a wireless devicedifficult in a small area [8].

In recent decades, reducing the size of antennas has been one of the main focuses inthe designers of antennas. Miniature antennas are especially used in micro-fabricationtechnologies to manufacture wireless devices [9,10]. In fact, the length of an ordinaryantenna that operates at some frequency is generally of the order of a half-wavelengthof that frequency [8], e.g., the conventional length of an antenna resonating at 1 GHz inthe case of a dielectric constant of 2.2 is approximately 100 mm. However, this length ispractically unacceptable for several devices. Moreover, most devices such as satellite, radio

Electronics 2022, 11, 2123. https://doi.org/10.3390/electronics11142123 https://www.mdpi.com/journal/electronics

Electronics 2022, 11, 2123 2 of 14

frequency identification (RFID) chips, and phones need the use of multiple antennas [11–14].Thus, the development of wireless devices will continue to challenge researchers to designsmaller antennas.

In the literature, there is a large variety of antennas that are used in various domainsof wireless systems. The simplest is the wire antenna that is used as a loop or dipoleantenna. Another antenna type is the aperture antenna that appears as a horn shape.Planar antennas, such as microstrip antennas, have been used extensively during the pastthree decades [15]. Many researchers have focused on microstrip patch antennas. Despitetheir narrow bandwidth, the microstrip patch antennas have many advantages comparedto other conventional antennas such as low manufacturing costs, low volume, weight, andthickness, simplicity of manufacturing, and the possibility of integrating discrete elements [16].

For several years, many studies have focused on the miniaturization of antennas.However, these techniques have been confronted with a difficult problem due to the gainand bandwidth fundamental limit that depends on the antenna size [17].

Generally, there are three principal ways to miniaturize an MPA: introducing slots,shorting and folding, and material loading. During the first method (introducing slots),the reduction of the size of a patch antenna can be realized by creating slots or changingthe shape of the patch. For the purpose of obtaining a large electrical length in a smallarea [18], miniaturized patches can be optimized using a genetic algorithm (GA) [17,19,20].However, this technique will be complicated because the geometry of the antenna and itsgain will be very low. Fractal geometries are employed to reduce the size of the microstrippatch antenna. However, this antenna suffers from a considerable reduction in band-width [21]. The second technique (shorting and folding) is the ground plane deformation.In this method, researchers use defected ground structures (DGSs) to miniaturize the an-tenna. In the literature, DGSs have several shapes: simples ones, e.g., spiral, H-shape, andU-shape or complex ones, for example, split-ring resonators (SRRs) [22,23]. The realizationmethod is simple but there is no standard design procedure and it provides a low efficiencyand a narrow bandwidth. The third and simplest method to miniaturize a patch antenna(material loading) is the utilization of a substrate with a high relative permittivity (εr), asthe antenna’s resonance frequency is scaled by 1/

√εrµr (µr is the relative permeability of

the substrate). Nevertheless, the last technique suffers from a decrease in the bandwidthwhen a substrate with a high relative permittivity is used [24–28].

The main contribution of this work is to propose a method for enhancing the per-formances of miniaturized microstrip patch antennas loaded with a thin-film materialwith high relative permittivity. First, a thin film was used to decrease the antenna’s res-onance frequency while keeping the antenna’s patch dimensions. Next, to enhance theantenna’s performance with a thin film, a GA-based method was employed to determinethe dimensions of the antenna’s patch. A GA is a robust searching and optimization tech-nique that can be applied to a wide range of electromagnetic problems. [29–31]. GAs areinspired by Darwinian ideas of natural selection and evolution. This technique has beenutilized to improve several performances of MPAs such as bandwidth [32], gain [33], andsize [19,23,34–36] and to reduce the maximum sidelobe level [37]. GAs have been used todesign an antenna for 5G applications, which requires operating at multiple bands whileoffering excellent gain and efficiency across all bands [38,39].

2. Methodology

For miniaturizing the MPA’s dimensions and enhancing its performance, a thin-filmmaterial with very high dielectric permittivity (εr2 = 250) and low loss (tan δ = 0.02), i.e.,ferroelectric material (B0.8S0.2TiO3) [40,41] is loaded into the patch. The flowchart in Figure 1shows the processing steps to integrate the thin-film material in the antenna.

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Figure 1. Flowchart of the antenna design method.

First, an ordinary antenna operating at 5.8 GHz is designed. The dimensions of thefirst design are determined on the basis of a published study [42]. Next, a thin-film materialwith different thicknesses is integrated to obtain a miniaturized antenna (that operates at4 GHz). Finally, the new MPA’s dimensions are optimized. The return loss is used as areference parameter in the GA-based optimization process to enhance the final designedantenna’s performances (return loss, bandwidth, and VSWR).

2.1. Initial Geometry of the Antenna

Figure 2 depicts the structure of the patch antenna’s initial geometry. This initialstructure has been studied in detail in [42]. For a microstrip patch antenna with D1 << L1,D1 << W1, and L1 > W1 > D1 (the length and width of the patch are L1 and W1, respectively,while the height of the substrate is D1), the dominant mode is the TM010 mode. Theresonance frequency (fr) and bandwidth (∆f ) are given [25,42,43] as follows:

fr =1

2L1√

εr√

ε0µ0(1)

∆ ffr≈ 3.77

εr − 1ε2

r

L1D1

λW1(2)

where µ0 and ε0 are the permeability and permittivity in free space, respectively, while λ isthe wavelength.

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Figure 2. The initial structure of the antenna (conventional microstrip patch antenna).

From Equation (1), the antenna’s size can be reduced by the use of a substrate with highrelative permittivity due to its refractive index (n =

√εr√

ε0µ0). For this reason, a thin-filmmaterial with high dielectric permittivity has been used. In this study, a ferroelectric materialthat has a relative permittivity εr2 = 250 has been used [38,41]. However, the use of a highrelative permittivity substrate results in a narrow bandwidth. Indeed, from Equation (2),one observes that as εr increases, the bandwidth decreases, as the stored electrical energyincreases [28]. To solve this problem, the values of the patch corresponding to the newantenna (antenna with the thin-film material) have been optimized. Then, the returnloss and VSWR are considered parameters in the GA-optimization process to enhanceits performance.

2.2. Antenna with the Thin-Film Material

Figure 3 depicts the structure of the studied antenna (with a thin-film material). Thiswas designed as a rectangular patch on two substrates. The first substrate (lower substrate)is FR-4 with thickness D1 equal to 1.58 mm and permittivity εr1 equal to 4.4. The secondsubstrate (upper substrate) consists of a thin film of a ferroelectric material B0.8S0.2TiO3 (BST)of thickness D2 and is characterized by a very high dielectric permittivity (εr2 = 250) [39,41].

Figure 3. Proposed microstrip patch antenna (a) directly on top and (b) side view.

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The goal of the work is the miniaturization of the antenna. The antenna’s dimensionsoperating at 5.8 GHz have been used for an antenna that operates at 4 GHz. Since theresonance frequency (fr) of a patch antenna is determined by the patch’s dimensions and thesubstrate’s relative permittivity [25,42], a thin film of BST was integrated, and its thicknesswas varied until the desired resonance frequency. Note that the desired resonance frequencyin this work is 4 GHz.

2.3. GA-Based Optimization of the Patch Parameters of the Antenna with the Thin-Film Material

A genetic algorithm is a stochastic search technique based on Darwin’s theory ofevolution [44]. This technique plays an important role in solving problems involvingpossible solutions in a large search space where classical methods cannot be used. Themost important advantage of GA over other methods such as Particle Swarm Optimization(PSO) is the accuracy [45]. Except for the drawback of being computationally expensivedue to the GA (time-consuming), this technique has the following advantages [46]:

- Optimization is possible for continuous or discrete variables,- Ability to work with a large number of variables,- It is well suited to parallel computers.- It works with numerically generated data, experimental data, or analytical functions.

GA uses the concepts of biological evolution to resolve optimization problems. Princi-ples based on gene combinations in biological reproduction are employed to repeatedlymodify a population of individual points. Thanks to its random nature, GA enhances thechances of identifying a global solution. Therefore, this proves to be extremely effectiveand stable while searching for global optimum solutions [47].

The purpose of a GA is to compute the extrema of a function identified in a spaceof data. An evolutionary process is utilized to resolve a problem using GA, in whichpossible solutions (chromosomes) will be utilized to develop new solutions. Such a groupof possible solutions will be named a population. For the objective of creating the nextgeneration of the population, only one population (particular) will succeed and will beemployed. Solutions utilized for creating novel solutions (offspring) are selected based ontheir fitness function. The chromosome with the greatest chance of reproducing is the mostsuited. Figure 4 shows the GA-based method flowchart to optimize the model parameters.The GA-base optimization method consists of the following steps [44]:

1. Generation of the initial population: A binary string is used to represent all chromo-somes, to exemplify chromosome X: 11110000 and chromosome Y: 11001100.

2. The initial population is produced via a mechanism capable of creating a non-homogeneous population of individuals that will serve as a basis for future gen-erations. Population size and the total number of individuals directly influence theconvergence of the GA. Because the goal is the identification of the optimal values ofL1 and W1, the population size is set to 30 and the number of individuals is set to 2.

3. Selection: The fitness function is used to evaluate and classify populations. For thenext generation, populations offering the best fitness rates will be selected.

4. Crossover: from parents, genes are recombined to form a novel chromosome; chro-mosome X: 11110000 and Chromosome Y: 11001100 might be crossed over after thefourth locus to form two new offspring chromosomes, 11111100 and 11000000. Witha constant probability (CP), the crossover is applied to the population. Usually, thisconstant relies on the application and it is very large [48]. In this work, CP has beenset to 0.9.

5. Mutation: for producing novel offspring, a few chromosome bits are changed. Inbinary encoding, some randomly selected bits may be modified from 1 to 0 or viceversa, for example, chromosome 11110110 can possibility be changed in the thirdlocation to produce chromosome 11110010. In general, in this operator, it is suitableto choose a low probability of mutation (MP). Typically, MP ranges from 0.01 to0.3. [48,49]. In this work, MP is equal to 0.05.

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6. Evaluation Function: for any optimization method, the evaluation function is themost important stage in the optimization procedure’s success. Moreover, it presentsthe relation between the physical problem to be optimized and the GA. The bestsolution is one, which diminishes the fitness function (σ). The aim of the study isthe minimization of the maximum return loss magnitude (S11) at three frequencies,f 1 = 3.8 GHz, fr = 4.0 GHz, and f 2 = 4.2 GHz (to widen the bandwidth while keepingthe resonance frequency at 4 GHz). In this case, σ is defined as [50]:

σ = min(S11n)∀n

(3)

where n is the index that refers to sample points in the S11 versus frequency function.

Figure 4. Schematic diagram of the GA algorithm.

In order to improve the antenna’s performance (with the thin film), GA was utilizedto optimize the microstrip antenna’s patch dimensions (L1 and W1). The purpose of theoptimization is to obtain the lowest S11 (dB) value at the resonance frequency of 4 GHz, aswell as a value less than −10 (dB) for f 1 and f 2. The stop condition is that the number ofgenerating iterations, which is set to 1000, is reached. The GA-based optimization processof the microstrip patch antenna with the thin film is illustrated in Figure 5.

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Figure 5. The GA-based optimization process of the microstrip patch antenna with a thin film.

The optimization process runs in two phases. Firstly, the design of the miniaturizedantenna begins via an antenna operating at 4 GHz by integrating a thin-film material withhigh effective permittivity whose bandwidth is 127.4 MHz, return loss is 19.42 dB, andVWSR is 1.86 as the initial state. In order to enhance these last performances (bandwidth,return loss, and VWSR), the GA method has been integrated for the optimization of thepatch dimensions to decrease the fitness function.

3. Results and Discussion3.1. Miniaturization of the Microstrip Patch Antenna

The numerical analysis was done with two electromagnetic simulators: High-FrequencyStructure Simulator (HFSS), which employs the finite element method in the frequencydomain, and Computer Simulation Technology (CST), which utilizes the finite integra-tion approach in the time domain. The antenna’s initial structure (conventional MPA) isdepicted in Figure 1 with its dimensions listed in Table 1.

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Table 1. Dimensions of the initial structure antenna.

W1 × L1(mm2)

W2 × L2(mm2)

W3 × L3(mm2)

D1(mm)

W2(mm)

15.9 × 11.26 0.71 × 13.31 3.02 × 11.81 1.58 0.01–0.1

Figure 6 presents the results of the proposed antenna without integration of the thin-film material obtained by HFSS and CST. The simulation results produced from the twosimulators indicate excellent agreement.

Figure 6. Simulated return loss of the proposed patch antenna without the thin film for the HFSS andCST simulators.

For comparison, Figure 7 displays the return loss of the patch antenna without andwith a thin-film material (10 µm thick of the ferroelectric material B0.8S0.2TiO3). Theantenna operating frequency shifted from 5.81 GHz to 5.31 GHz for the HFSS and CSTsimulators. This variation is a consequence of the variation in the effective permittivity ofthe ferroelectric–dielectric substrate due to the integration of a thin-film material, whichhas a relative permittivity of 250.

Figure 7. The simulated return loss of the patch antenna without and with a thin film of theferroelectric material BST for HFSS and CST.

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For the purpose of studying the effect of the thickness of the thin-film material on theantenna’s performance, a parametric analysis was carried out by varying the thickness (D2).Figure 8 depicts the results. It can be seen that the resonance frequency changes with thechange in the thin-film thickness. The thicker the film, the more the resonance frequencydecreases. For example, the resonance frequency for D2 = 0.01 mm is 5.31 GHz and it shiftsto 4.00 GHz when D2 = 0.1 mm. These results present an excellent agreement with previousresults [24–30].

Figure 8. Thickness effects of the thin-film material on the return loss of the antenna.

For the aim of measuring the reduction rate related to the antenna size, a novel antenna(conventional antenna) was designed to operate at 4 GHz, i.e., at the operating frequency ofthe designed antenna. After studying this antenna’s performance, it appears that the patchmust have the following dimensions: W1 = 22.82 mm and L1 = 17.40 mm. For comparingthe two antenna sizes operating at 4 GHz, two rectangular are drawn in Figure 9. The darkrectangle depicts the microstrip patch antenna with a thin-film material, whereas the lightrectangular represents a conventional antenna (without the thin film). This figure showsthat the miniaturization approach proposed in this study delivers a 55% reduction in thesize of the radiating element.

Figure 9. Antenna size comparison at 4 GHz: the light-colored rectangle represents the ordinarymicrostrip patch; the dark-colored rectangle represents the microstrip patch antenna with the thin film.

Figure 10 presents the radiation pattern (for the two principal plans E and H). Thefigure shows the principal plans at 4 GHz. Phi = 0◦ and theta = all values for plan E,

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compared with phi = 90◦ and theta = all values for the H plan. The radiation pattern revealsthat those antennas (proposed and conventional) have nearly the same radiation behavior,and the proposed antenna has an approximately omnidirectional characteristic for theE plan and H plan, especially at the resonance frequency (4 GHz).

Figure 10. Microstrip patch antenna radiation patterns with and without a thin-film material at4 GHz; (a) E plan, (b) H plan.

3.2. GA-Optimization of the Antenna with a Thin-Film Material with High Permittivity

The GA method was then used to enhance the performance of the miniaturizedantenna by minimizing the fitness function in the second phase. For comparison, the resultsare given in Figure 11 and Table 2. The proposed antenna conserved about 40% of the globalarea of the conventional antenna at a resonance frequency of 4 GHz, and its performancein terms of the return loss and VSWR improved by about −10.39 dB and 0.8, respectively,thus increasing the bandwidth of the antenna by 29.4 MHz.

Figure 11. Comparison between the microstrip patch antenna with thin-film material before and afterGA-optimization.

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Table 2. Comparison of the proposed microstrip patch antenna operating at 4 GHz with the conven-tional antenna, antenna with a thin film, and antenna with a thin film optimized by GA.

Antenna Patch Dimensions (W1 × L1)(mm2)

ReturnLoss (dB)

Bandwith(MHz) VSWR Gain

(dB)

Conventional 22.1 × 16.84 (372.16) −33.7 118.4 1.04 3.88With thin film 15.90 × 11.26 (179.04) −19.42 127.4 1.86 3.89

With thin film and GA 13.76 × 11.32 (155.04) −29.81 147.8 1.06 3.63

Table 3 illustrates a quantitative comparison between the proposed antenna and minia-turized antennas reported in previously published works in terms of different parameters(gain, rate of miniaturization, return loss, and bandwidth). It is worth noting that in termsof bandwidth, the proposed miniaturized patch antenna outperformed the reference anten-nas. The performances of the proposed antenna were comparable to those of the referenceantennas in terms of the return loss, rate of miniaturization, and gain. The initial and finalresonance frequencies of the suggested antenna and reference antennas are also given inTable 3. In comparison to the reference antennas provided in Table 3, the proposed antennareflects a compromise between the size, gain, and bandwidth.

Table 3. Comparison of the proposed antenna with reference miniaturized patch antennas.

Antenna far

(GHz)fbr

(GHz)Return

Loss (dB)Bandwith

(MHz)Miniaturization

Rate (%)Gain(dB)

N. Herscovici et al. [19] 3.000 1.738 −24 10 42 1P. Soontornpipit et al. [51] 0.405 0.4035 −15 20 / /

H. Elftouh et al. [35] 5.700 3.000 ≈−38 / 50 2.14M. Lamsalli et al. [34] 4.900 2.160 −20 ≈10 82 5.82

M. S. Sharawi et al. [22] 5.040 2.450 ≈−25 ≈50 76 −0.8

M K. Dhakshinamoorthi et al. [36]3.000 2.3958 −12.5 30 24.4 /4.000 2.4022 −13.5 22 48.5 /

Proposed 5.800 4.000 −29.81 147.8 ≈60 3.63

4. Conclusions

A new method for designing and optimizing miniaturized microstrip patch antennasfor application in telecommunications is presented. A thin-film material with high per-mittivity is used for reducing the antenna’s resonance frequency while keeping the patchdimensions of the antenna constant. For the purpose of enhancing the performances ofminiaturized antennas, a genetic algorithm is utilized for the estimation of the optimalparameters of the patch of the antenna with a thin film. The results indicate that the de-signed antenna’s resonance frequency changed from 5.8 GHz to 4.0 GHz and the area of theproposed antenna was reduced by around 60%, especially in comparison to a conventionalantenna alone without thin film. Therefore, most of the performances of the proposedantenna such as the bandwidth, return loss, gain, and VSWR improved.

Finally, the performances of the proposed antenna were compared to those of referenceantennas reported in the literature. It is worth noting that the proposed method can beeasily exploited for designing filters or antennas with diverse frequencies or geometries.The use of an alternative optimization method, such as particle swarm optimization (PSO),and other thin-film materials such as KTN (KTa1-xNbxO3) can be investigated in futurework to improve the performance of the miniaturized microstrip patch antenna.

Author Contributions: Conceptualization, M.B. and A.R.; Data curation, M.B., A.R., A.K., K.H.-C.and M.S.A.; Formal analysis, M.B., K.H.-C., T.E.A.A. and M.S.A.; Funding acquisition, T.E.A.A., M.A.and S.S.M.G.; Investigation, M.B., K.H.-C., T.E.A.A., M.A. and S.S.M.G.; Methodology, A.R. and A.K.;Project administration, M.B., M.S.A. and S.S.M.G.; Resources, T.E.A.A., M.S.A., M.A. and S.S.M.G.;Software, M.B., A.R., A.K. and K.H.-C.; Supervision, M.B. and S.S.M.G.; Validation, A.K., M.S.A. andM.A.; Visualization, K.H.-C., T.E.A.A. and S.S.M.G.; Writing—original draft, M.B., A.R., A.K. and

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K.H.-C.; Writing—review & editing, T.E.A.A., M.S.A., M.A. and S.S.M.G. All authors have read andagreed to the published version of the manuscript.

Funding: This research received funding from Taif University Researchers Supporting Project TURSP2020/34, Taif University, Taif, Saudi Arabia.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: Not applicable.

Acknowledgments: The authors appreciate Taif University Researchers Supporting Project TURSP2020/34, Taif University, Taif, Saudi Arabia for supporting this research.

Conflicts of Interest: The authors declare no conflict of interest.

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