Circuit-Simulation-Based Design Optimization of 3.5 GHz Doherty Power Amplifier via Multi-Objective Evolutionary Algorithm and Unified Optimization Framework

Chen, Chao-Hsuan; Yang, Ya-Shu; Chen, Chieh-Yang; Hsieh, Yun-Che; Tsai, Zuo‐Min; Li, Yiming

doi:10.1109/rfit49453.2020.9226245

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…In the past few years, a number of intelligent algorithms have been applied successfully to DPA optimization, which include support vector machines, 21 Bayesian optimization, 22 and others. 23 When compared to these algorithms, the particle swarm optimization (PSO) algorithm does not require a surrogate model as part of the optimization process, which greatly reduces the complexity of the algorithm. Moreover, the PSO algorithm has strong global optimization capabilities, high convergence, and is extensible in a straightforward manner.…”

Section: Introductionmentioning

confidence: 99%

Automatic multi‐objective particle swarm optimization method for effective Doherty power amplifier design

Hao,

Qu,

Huang

et al. 2024

Int J Numerical Modelling

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In this paper, an automatic multi‐objective particle swarm optimization (AMOPSO) method is developed for the design of Doherty power amplifiers (DPAs). In comparison to the well‐known built‐in optimizer available in commercial simulators, the proposed method not only reduces the optimization time, but also provides superior power added efficiency (PAE) for the final power amplifier. According to the reported measurements, the output PAE of the fabricated DPA exceeds 50% at 6 dB backoff, the saturated PAE is more than 61%, and the saturated output power (Pout) is over 43 dBm in the target frequency range of 1.9–2.1 GHz. As compared with existing optimization methods, the proposed method allows reducing optimization time by more than 37%.

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Section: Introductionmentioning

confidence: 99%

Automatic multi‐objective particle swarm optimization method for effective Doherty power amplifier design

Hao,

Qu,

Huang

et al. 2024

Int J Numerical Modelling

View full text Add to dashboard Cite

show abstract

“…Some works show the applicability of evolutionary computing in analog circuit design and possibilities for time controlling [3], achieving efficient and flexible design [4], and design optimization [5]. Bayesian optimization techniques are also under investigation, pointing out another different possible approach for improving the circuit design process regarding design speed and accuracy [6,7].…”

Section: Introductionmentioning

confidence: 99%

M-Polar Fuzzy Graphs and Deep Learning for the Design of Analog Amplifiers

Ivanova

Durcheva

2023

Mathematics

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The design of analog circuits is a complex and repetitive process aimed at finding the best design variant. It is characterized by uncertainty and multivariate approaches. The designer has to make different choices to satisfy a predefined specification with required parameters. This paper proposes a method for facilitating the design of analog amplifiers based on m-polar fuzzy graphs theory and deep learning. M-polar fuzzy graphs are used because of their flexibility and the possibility to model different real-life multi-attribute problems. Deep learning is applied to solve a regression task and to predict the membership functions of the m-polar fuzzy graph vertices (the solutions), taking on the role of domain experts. The performance of the learner is high since the obtained errors are very small: Root Mean Squared Error is from 0.0032 to 0.0187, Absolute Error is from 0.022 to 0.098 and Relative Error is between 0.27% and 1.57%. The proposed method is verified through the design of three amplifiers: summing amplifier, subtracting amplifier, and summing/subtracting amplifier. The method can be used for improving the design process of electronic circuits with the possibility of automating some tasks.

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Application of Community Detection Based Parallel MOEA/D Algorithm in RF Power Amplifier Circuit Design

Zhou,

Li,

Bao

et al. 2024

2024 Conference of Science and Technology for Integrated Circuits (CSTIC)

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Circuit-Simulation-Based Design Optimization of 3.5 GHz Doherty Power Amplifier via Multi-Objective Evolutionary Algorithm and Unified Optimization Framework

Cited by 4 publications

References 11 publications

Automatic multi‐objective particle swarm optimization method for effective Doherty power amplifier design

Automatic multi‐objective particle swarm optimization method for effective Doherty power amplifier design

M-Polar Fuzzy Graphs and Deep Learning for the Design of Analog Amplifiers

Application of Community Detection Based Parallel MOEA/D Algorithm in RF Power Amplifier Circuit Design

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