A Genetic algorithm for the optimal design of a multistage amplifier

Beqal, Asmae El; Benhala, Bachir; Zorkani, Izeddine

doi:10.11591/ijece.v10i1.pp129-138

Cited by 20 publications

(13 citation statements)

References 12 publications

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“…The maximum value of the total leakage current is dominated by the subthreshold leakage and equals to 54.102×10 -9 A. More details and more references related to the nanoscale transistor could be found in [22][23][24][25].…”

Section: Band-to-band Tunneling Leakage Current Comparisons and Simulmentioning

confidence: 99%

Accurate leakage current models for MOSFET nanoscale devices

Rjoub¹,

Mistarihi²,

Taradeh³

2020

IJECE

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This paper underlines a closed forms of MOSFET transistor’sleakage current mechanisms inthe sub 100nmparadigm.The incorporation of draininduced barrier lowering (DIBL), Gate Induced Drain Lowering (GIDL) and body effect (m) on the sub-threshold leakage (Isub) wasinvestigated in detail. The Band-To-Band Tunneling (IBTBT) due to the source and Drain PN reverse junction were also modeled witha close and accurate model using a rectangularapproximation method (RJA). The three types of gate leakage (IG) were also modeled and analyzed for parasitic (IGO), inversion channel (IGC), and gate substrate (IGB).In addition, the leakage resources due to the aggressive reduction in the oxide thickness (<5nm) have been investigated. Simulation results using HSPICEexhibits a tremendous agreement with the BSIM4 model. The dominant value of the sub-threshold leakage was due to the DIBL and GIDL effects. Various recommendations regarding minimizing the leakage current at both device level and the circuit level were suggested at the end of this paper

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Section: Band-to-band Tunneling Leakage Current Comparisons and Simulmentioning

confidence: 99%

Accurate leakage current models for MOSFET nanoscale devices

Rjoub¹,

Mistarihi²,

Taradeh³

2020

IJECE

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“…These practises typically result in finding an optimal or nearoptimal global solution to a given problem [1], [2]. There are various types of crossover operators which are [3]- [5]. In this review, the main emphasis is on an important type of problems with combinatorial optimization whose solutions can be expressed with permutation.…”

Section: Introductionmentioning

confidence: 99%

A self adaptive new crossover operator to improve the efficiency of the genetic algorithm to find the shortest path

Chattoraj

Vinayakamurthy

2021

IJEECS

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<p>Route planning is an important part of road network. To select an optimized route several factors such as flow of traffic, speed limits of road. are concerned. Total cost of such a network depends on the number of junctions between the source and the destination. Due to the growth of the nodes in the network it becomes a tough job to determine the exact path using deterministic algorithms so in such cases genetic algorithms (GA) plays a vital role to find the optimized route. Crossover is an important operator ingenetic algorithm. The efficiency of thegenetic algorithmis directlyinfluenced by the time of a crossover operation. In this paper a new crossoveroperator closest-node pairing crossover (CNPC) is recommended which is explicitly designed to improve the performance of the genetic algorithm compared to other well-known crossover operators such as point based crossover and order crossover. The distance aspect of the network problem has been exploited in this crossover operator. This proposed technique gives a better result compared to the other crossover operator with the fitness value of 0.0048. The CNPC operator gives better rate of convergence compared to the other crossover operators.</p>

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“…In this field, the methods mostly used are EA: 'Evolutionary Algorithms ' [7], such as the Differential Evolution (DE) Algorithm [8], and the Genetic Algorithm (GE) [9], [10], but in the last two decades, a new group of nature-inspired heuristic optimization algorithms have been introduced as SI: 'Swarm Intelligence Techniques', such as Ant Colony Optimization (ACO) [11], [12], Gravitational Search Algorithm (GSA) [13], Artificial Bee Colony (ABC) [14], Dragonfly Algorithm (DA) [15], Particle Swarm Optimization (PSO) [16], Grey Wolf Optimizer (GWO) [17], and Bacterial Foraging Optimization (BFO) [18].…”

Section: Introductionmentioning

confidence: 99%

Shape Optimization of Planar Inductors for RF Circuits using a Metaheuristic Technique based on Evolutionary Approach

Hajjami¹,

Benhala²,

Bouyghf³

2020

Adv. sci. technol. eng. syst. j.

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In this article, we concentrate on the use of a metaheuristic technique based on an Evolutionary Algorithm (EA) for determining the optimal geometrical parameters of spiral inductors for RF circuits. For this purpose, we have opted for an optimization procedure through an enhanced Differential Evolution (DE) algorithm. The proposed tool allows the design of optimized integrated inductors not only with a maximum quality factor(Q), but also with a maximum self-resonant frequency (SRF), and a minimum surface area, in addition to being adapted to any model of any technology. This paper presents also a comparison between performances of the optimized inductors (inductor square shape and inductor circular shape), in terms of the quality factor, SRF, and circuit size. For the purpose of mitigating the impact of parasitic effects, design basics have been taken into consideration. Then, in order to investigate the efficacy of evaluated results, an (EM) simulator has been employed.

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A Genetic algorithm for the optimal design of a multistage amplifier

Cited by 20 publications

References 12 publications

Accurate leakage current models for MOSFET nanoscale devices

Accurate leakage current models for MOSFET nanoscale devices

A self adaptive new crossover operator to improve the efficiency of the genetic algorithm to find the shortest path

Shape Optimization of Planar Inductors for RF Circuits using a Metaheuristic Technique based on Evolutionary Approach

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