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Forward and reverse modeling of low noise amplifiers based on circuit simulations

  • Luciano De Tommasi
  • , Joost Rommes
  • , Theo Beelen
  • , Marcel Sevat
  • , Jeroen A. Croon
  • , Tom Dhaene
  • NXP Semiconductors
  • Ghent University

Research output: Chapter in Book/Report/Conference proceedingsChapterpeer-review

Abstract

Forward and reverse modeling of RF circuit blocks are useful approaches in design space exploration. The underlying idea of forward modeling is the creation of accurate surrogate models, which can be used to predict the circuit performances replacing (expensive) circuit simulations. On the other hand, reverse modeling concerns multiobjective optimization to explore relevant trade-offs between performances. This paper provides a discussion of application of surrogate models and multiobjective optimization to narrow-band low noise amplifier design. We discuss numerical difficulties encountered when the forward model is derived by using surrogate models of low noise amplifier admittances to compute performance figures via analytical equations. Afterward, we provide an example where direct performace modeling leads to a more accurate result even when the simplest surrogate model type (a lookup table) is used. Finally, a detailed tutorial of the normal boundary intersection optimization method is provided.

Original languageEnglish
Title of host publicationModel Reduction for Circuit Simulation
Pages111-124
Number of pages14
DOIs
Publication statusPublished - 2011
EventWorkshop on Model Reduction for Circuit Simulation - Hamburg, Germany
Duration: 30 Oct 200831 Oct 2008

Publication series

NameLecture Notes in Electrical Engineering
Volume74 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceWorkshop on Model Reduction for Circuit Simulation
Country/TerritoryGermany
CityHamburg
Period30/10/0831/10/08

Keywords

  • Design space exploration
  • Forward modelling
  • Low noise amplifier
  • Multiobjective optimization
  • Pareto front
  • Response surface modelling
  • Reverse modelling
  • Surrogate modelling

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