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A two-step surrogate modeling strategy for single-objective and multi-objective optimization of radiofrequency circuits

  • F. Passos
  • R. González-Echevarría
  • E. Roca
  • R. Castro-López
  • F. V. Fernández
Methodologies and Application
  • 78 Downloads

Abstract

The knowledge-intensive radiofrequency circuit design and the scarce design automation support play against the increasingly stringent time-to-market demands. Optimization algorithms are starting to play a crucial role; however, their effectiveness is dramatically limited by the accuracy of the evaluation functions of objectives and constraints. Accurate performance evaluation of radiofrequency passive elements, e.g., inductors, is provided by electromagnetic simulators, but their computational cost makes their use within iterative optimization loops unaffordable. Surrogate modeling strategies, e.g., Kriging, support vector machines, artificial neural networks, etc., arise as a promising modeling alternative. However, their limited accuracy in this kind of applications has prevented a widespread use. In this paper, inductor performance properties are exploited to develop a two-step surrogate modeling strategy in order to evaluate the behavior of inductors with high efficiency and accuracy. An automated design flow for radiofrequency circuits using this surrogate modeling of passive components is presented. The methodology couples a circuit simulator with evolutionary computation algorithms such as particle swarm optimization, genetic algorithm or non-dominated sorting genetic algorithm (NSGA-II). This methodology ensures optimal performances within short computation times by avoiding electromagnetic simulations of inductors during the entire optimization process and using a surrogate model that has less than 1% error in inductance and quality factor when compared against electromagnetic simulations. Numerous real-life experiments of single-objective and multi-objective low-noise amplifier design demonstrate the accuracy and efficiency of the proposed strategies.

Keywords

Surrogate modeling Machine learning Single-objective optimization Multi-objective optimization Application to electronic circuit design Integrated inductors 

Notes

Acknowledgements

This work was supported in part by the TEC2013-45638-C3-3-R and TEC2016-75151-C3-3-R Projects (funded by the Spanish Ministry of Economy, Industry and Competitiveness and ERDF) and in part by the P12-TIC-1481 Project (funded by Junta de Andalucia).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Human and animal participants

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • F. Passos
    • 1
  • R. González-Echevarría
    • 1
  • E. Roca
    • 1
  • R. Castro-López
    • 1
  • F. V. Fernández
    • 1
  1. 1.Instituto de Microelectrónica de Sevilla, IMSE-CNMCSIC and Universidad de SevillaSevilleSpain

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