In this work we demonstrate a feed-forward Artificial Neural Network (ANN) model for microwave active devices, here an FET, where the input and outputs are the device harmonic port waves. The ANN has been trained through an in-house tool implementing back-propagation and gradient descent, with load-pull data sweeping the available input power from back-off to harsh compression. The ANN model can be seamlessly used as a replacement of any active device model in microwave CAD tools implementing the Harmonic Balance (HB) algorithm. As a demonstrator, we developed in MATLAB an HB circuit solver implementing the ANN device model within an external circuit simulating the device with different loading conditions. The ANN modeling approach been successfully applied both in class A and a class B bias.

Frequency-domain ANN Non-linear Active Device Model for Harmonic-Balance-based CAD / Ramella, Chiara; Corbellini, Simone; Guerrieri, Simona Donati; Pirola, Marco. - (2025), pp. 1-4. (Intervento presentato al convegno IEEE MTT-S Latin America Microwave Conference, LAMC 2025 tenutosi a San Juan (USA) nel 22-24 January 2025) [10.1109/lamc63321.2025.10880563].

Frequency-domain ANN Non-linear Active Device Model for Harmonic-Balance-based CAD

Ramella, Chiara;Corbellini, Simone;Guerrieri, Simona Donati;Pirola, Marco
2025

Abstract

In this work we demonstrate a feed-forward Artificial Neural Network (ANN) model for microwave active devices, here an FET, where the input and outputs are the device harmonic port waves. The ANN has been trained through an in-house tool implementing back-propagation and gradient descent, with load-pull data sweeping the available input power from back-off to harsh compression. The ANN model can be seamlessly used as a replacement of any active device model in microwave CAD tools implementing the Harmonic Balance (HB) algorithm. As a demonstrator, we developed in MATLAB an HB circuit solver implementing the ANN device model within an external circuit simulating the device with different loading conditions. The ANN modeling approach been successfully applied both in class A and a class B bias.
2025
979-8-3315-4040-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2999212