We present a novel technique based on artificial neural networks (ANNs), for design and optimization of active RF filters based on high-Q Active Inductors (AIs). ANNs are used to build the small-signal S-parameter model as well as the noise model of the transistor considered for the AI, by considering variable current and voltage biasing. With this approach, it is possible to rapidly identify the best design solution that lead to an optimal network configuration and filter order at the initial design steps. To demonstrate the advantage of this method, a bandpass filter was designed by means of a custom ANN model, made in MATLAB environment that is based on a commercial transistor, the BFP840ESD from Infineon. With this technique, we easily optimized the design to achieve an operating frequency of 5 GHz with a 3 dB bandwidth of about 30 MHz with a high Q-factor and a minimum noise figure of 0.96 dB at the central frequency.

Artificial neural networks approach to active inductor-based filter design

Pantoli L.;Leoni A.;Stornelli V.;Leuzzi G.
2018-01-01

Abstract

We present a novel technique based on artificial neural networks (ANNs), for design and optimization of active RF filters based on high-Q Active Inductors (AIs). ANNs are used to build the small-signal S-parameter model as well as the noise model of the transistor considered for the AI, by considering variable current and voltage biasing. With this approach, it is possible to rapidly identify the best design solution that lead to an optimal network configuration and filter order at the initial design steps. To demonstrate the advantage of this method, a bandpass filter was designed by means of a custom ANN model, made in MATLAB environment that is based on a commercial transistor, the BFP840ESD from Infineon. With this technique, we easily optimized the design to achieve an operating frequency of 5 GHz with a 3 dB bandwidth of about 30 MHz with a high Q-factor and a minimum noise figure of 0.96 dB at the central frequency.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/148352
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