Artificial neural network nonlinear transistor behavioral models: structure and parameter determination process based on the Cardiff model

Tian, Mengyue, Bell, James J. ORCID: https://orcid.org/0000-0002-4815-2199, Quaglia, Roberto ORCID: https://orcid.org/0000-0003-3228-301X, Azad, Ehsan M. and Tasker, Paul J. ORCID: https://orcid.org/0000-0002-6760-7830 2024. Artificial neural network nonlinear transistor behavioral models: structure and parameter determination process based on the Cardiff model. IEEE Transactions on Microwave Theory and Techniques 10.1109/TMTT.2024.3434959

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Abstract

This article introduces a novel artificial neural network (ANN) structure determination process based on the Cardiff model (CM), to determine ANN-based transistor nonlinear behavioral models. By relating the CM formulation and coefficients to the Taylor series expansion of the ANN model, a novel approach for determining the required values of a fully connected cascaded (FCC) ANN structure has been formulated. The proposed method provides the chance to escape from the possible time-consuming ANN determination process. Experiments proved that the proposed ANN models using the determination method can provide accurate prediction for the behavior acquired from load–pull characterizations of a Wolfspeed 10-W packaged gallium nitride (GaN) high electron mobility transistor (HEMT) simulation at 3.5 GHz, and a dense load–pull measurement of WIN NP12 4 × 75 μ m GaN HEMT at 20 GHz, with normalized mean square error (NMSE) levels lower than − 40 dB.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Engineering
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	0018-9480
Date of First Compliant Deposit:	30 August 2024
Date of Acceptance:	25 July 2024
Last Modified:	31 Aug 2024 07:16
URI:	https://orca.cardiff.ac.uk/id/eprint/171462

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