2026-05-28T03:45:58Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/280322026-02-03T10:58:06Zcom_10630_2254col_10630_37953

Automatic Extraction of Measurement-Based Large-Signal FET Models by Nonlinear Function Sampling. Martín-Guerrero, Teresa María Santarelli, Alberto Gibiino, Gian Piero Traverso, Pier Andrea Camacho-Peñalosa, Carlos Filicori, Fabio Transistores de efecto de campo Semiconductores A new method is proposed for the accurate experimental characterization and fully automated extraction of compact nonlinear models for Field-Effect Transistors (FETs). The approach, which leads to a charge-conservative description, is based on a single large-signal measurement under a two-tone sinusoidal wave excitation. A suitable choice of tone frequencies, amplitudes, and bias allows to adequately characterize the transistor over the whole safe operating region. The voltage controlled nonlinear functions describing the two-port FET model can be computed over an arbitrarily dense voltage domain by solving an overdetermined system of linear equations. These equations are expressed in terms of a new Nonlinear Function Sampling operator based on a bi-periodic Fourier series description of the acquired frequency spectra. The experimental validation is carried out on a 0.25-μm Gallium Nitride (GaN) on Silicon Carbide (SiC) High-Electron Mobility Transistor (HEMT) under continuous-wave (CW) and two-tone excitation (intermodulation distortion test). 2023-11-15T10:47:50Z 2023-11-15T10:47:50Z 2023-11-15 2020-03-27 journal article T. M. Martín-Guerrero, A. Santarelli, G. P. Gibiino, P. A. Traverso, C. Camacho-Peñalosa and F. Filicori, "Automatic Extraction of Measurement-Based Large-Signal FET Models by Nonlinear Function Sampling," in IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 5, pp. 1627-1636, May 2020, doi: 10.1109/TMTT.2020.2968886. https://hdl.handle.net/10630/28032 10.1109/TMTT.2020.2968886 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional IEEE