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Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine.

Shaohua Zhou1,2, Cheng Yang1,2, Jian Wang1,2

  • 1School of Microelectronics, Tianjin University, Tianjin 300072, China.

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Amplifier performance degrades with temperature, impacting system reliability. Modeling these temperature characteristics using extreme learning machines (ELM) improves system design and reduces testing costs.

Keywords:
ELMRF amplifiermodelingtemperature characteristics

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Area of Science:

  • Electrical Engineering
  • Materials Science

Background:

  • Amplifiers are critical in radio frequency (RF) systems.
  • Temperature-induced degradation of amplifier specifications can cause system failure.

Purpose of the Study:

  • To model the temperature-dependent characteristics of amplifier specifications.
  • To integrate amplifier degradation models into system optimization design.

Main Methods:

  • Utilized an extreme learning machine (ELM) to model temperature characteristics.
  • Collected measurement data for two distinct amplifiers.

Main Results:

  • The ELM model accurately reflected measured amplifier performance across temperatures.
  • The modeling approach significantly reduced measurement time and associated costs.

Conclusions:

  • ELM is effective for modeling temperature effects on amplifier specifications.
  • Accurate modeling facilitates improved system design and reliability engineering.