scholarly journals Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 787
Author(s):  
Amel Garbaya ◽  
Mouna Kotti ◽  
Mourad Fakhfakh ◽  
Esteban Tlelo-Cuautle
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Noise Figure ◽  
Low Noise ◽  
Low Noise Amplifiers ◽  
Scattering Parameters ◽  
Perfect Agreement ◽  
Swarm Optimization ◽  
The Third ◽  
Intercept Point

In this article we deal with the optimal sizing of low-noise amplifiers (LNAs) using newly proposed metamodeling techniques. The main objective is to construct metamodels of main performances of the LNAs (namely, the third intercept point (IIP3), the scattering parameters (Sij), and the noise figure (NF)) and use them inside an optimization kernel for maximizing the circuits’ performances. The kriging surrogate modelling technique is used for constructing these models. The particle swarm optimization (PSO) technique is considered as the optimization metaheuristic. Two CMOS amplifiers are considered: a UMTS LNA and a multistandard LNA. Obtained results show that, at the considered working frequencies, the first LNA exhibits at 2.14 GHz a noise figure of 1.30 dB, an S21 of 16.01 dB, an S11 of −12.60 dB, and an IIP3 of 8.30 dBm. At 2 GHz, the second LNA has a noise figure of 1.24 dB, an S21 of 17.16 dB, an S11 of −13.74 dB, and an IIP3 of 4.30 dBm. Comparisons between results obtained using the constructed models and those of the simulation are presented to show the perfect agreement between them.

scholarly journals 5GHz MMIC LNA Design Using Particle Swarm Optimization

2013 ◽  
Vol 5 (6) ◽  
pp. 257-262
Author(s):  
Low Wen Shin ◽  
Arjuna Marzuki .
Keyword(s):  
Particle Swarm Optimization ◽  
Integrated Circuit ◽  
Noise Figure ◽  
Particle Swarm ◽  
Low Noise ◽  
Design System ◽  
Stability Factor ◽  
Power Gain ◽  
Swarm Optimization ◽  
5 Ghz

This research presents an optimization study of a 5 GHz Monolithic Microwave Integrated Circuit (MMIC) design using Particle Swarm Optimization (PSO). MMIC Low Noise Amplifier (LNA) is a type of integrated circuit device used to capture signal operating in the microwave frequency. This project consists of two stages: implementation of PSO using MATLAB and simulation of MMIC design using Advanced Design System (ADS). PSO model that mimics the biological swarm behavior is developed to optimize the MMIC design variables in order to achieve the required circuit performance and specifications such as power gain, noise figure, drain current and circuit stability factor. Simulation results show that the proposed MMIC design fulfills the circuit stability factor and achieves a power gain of 19.73dB, a noise figure of 1.15 dB and a current of 0.0467A.

scholarly journals A Novel Design and Optimization Approach for Low Noise Amplifiers (LNA) Based on MOST Scattering Parameters and the gm/ID Ratio

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 785
Author(s):  
Juan L. Castagnola ◽  
Fortunato C. Dualibe ◽  
Agustín M. Laprovitta ◽  
Hugo García-Vázquez
Keyword(s):  
Integrated Circuits ◽  
Noise Figure ◽  
Low Noise ◽  
Low Noise Amplifiers ◽  
Optimization Approach ◽  
Scattering Parameters ◽  
Rf Integrated Circuits ◽  
Design And Optimization ◽  
Noise Amplifier

This work presents a new design methodology for radio frequency (RF) integrated circuits based on a unified analysis of the scattering parameters of the circuit and the gm/ID ratio of the involved transistors. Since the scattering parameters of the circuits are parameterized by means of the physical characteristics of transistors, designers can optimize transistor size and biasing to comply with the circuit specifications given in terms of S-parameters. A complete design of a cascode low noise amplifier (LNA) in MOS 65 nm technology is taken as a case study in order to validate the approach. In addition, this methodology permits the identification of the best trade-off between the minimum noise figure and the maximum gain for the LNA in a very simple way.

Optimal Design and Control of a Slider-Crank Mechanism System

2012 ◽  
Vol 487 ◽  
pp. 608-612 ◽  
Author(s):  
Chih Cheng Kao
Keyword(s):  
Particle Swarm Optimization ◽  
System Identification ◽  
Optimal Design ◽  
Fitness Function ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Pm Synchronous Motor ◽  
And Control ◽  
Crank Mechanism

This paper mainly proposes an efficient modified particle swarm optimization (MPSO) method, to identify a slider-crank mechanism driven by a field-oriented PM synchronous motor. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In system identification, we adopt the MPSO method to find parameters of the slider-crank mechanism. This new algorithm is added with “distance” term in the traditional PSO’s fitness function to avoid converging to a local optimum. Finally, the comparisons of numerical simulations and experimental results prove that the MPSO identification method for the slider-crank mechanism is feasible.

scholarly journals Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2000
Author(s):  
Jin-Hwan Lee ◽  
Woo-Jung Kim ◽  
Sang-Yong Jung
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Cost Function ◽  
Particle Swarm ◽  
Torque Ripple ◽  
Rate Of Change ◽  
Swarm Optimization ◽  
Traction Motor ◽  
Target Performance ◽  
The Cost

This paper proposes a robust optimization algorithm customized for the optimal design of electric machines. The proposed algorithm, termed “robust explorative particle swarm optimization” (RePSO), is a hybrid algorithm that affords high accuracy and a high search speed when determining robust optimal solutions. To ensure the robustness of the determined optimal solution, RePSO employs the rate of change of the cost function. When this rate is high, the cost function appears as a steep curve, indicating low robustness; in contrast, when the rate is low, the cost function takes the form of a gradual curve, indicating high robustness. For verification, the performance of the proposed algorithm was compared with those of the conventional methods of robust particle swarm optimization and explorative particle swarm optimization with a Gaussian basis test function. The target performance of the traction motor for the optimal design was derived using a simulation of vehicle driving performance. Based on the simulation results, the target performance of the traction motor requires a maximum torque and power of 294 Nm and 88 kW, respectively. The base model, an 8-pole 72-slot permanent magnet synchronous machine, was designed considering the target performance. Accordingly, an optimal design was realized using the proposed algorithm. The cost function for this optimal design was selected such that the torque ripple, total harmonic distortion of back-electromotive force, and cogging torque were minimized. Finally, experiments were performed on the manufactured optimal model. The robustness and effectiveness of the proposed algorithm were validated by comparing the analytical and experimental results.

Sign in / Sign up

Export Citation Format

Share Document