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HEMT noise neural model based on bias conditions

Vera Marković (University of Nis, Serbia and Montenegro)
Zlatica Marinković (University of Nis, Serbia and Montenegro)


Knowledge of the microwave transistor parameters at various bias conditions is often required in computer‐aided design of complex microwave low‐noise circuit. Since the measurements of noise parameters are very complex and time‐consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure.



Marković, V. and Marinković, Z. (2004), "HEMT noise neural model based on bias conditions", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 23 No. 2, pp. 426-435.



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