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Artificial neural networks for temperature dependent noise modeling of microwave transistors

In this paper an automated procedure for prediction of microwave transistor noise parameters versus temperature is presented. It is based on an improved Pospieszalski's noise model. In order to avoid extraction of device noise model equivalent circuit parameters (ECP) from the measured scattering and noise parameters for each operating temperature, an artificial neural network is introduced for modeling of the ECP temperature dependence. Therefore, it is necessary to acquire the measured data and extract the ECP only for several operating temperatures used for the network training. Once the network is trained and assigned to the considered noise model, the device noise parameters are easily obtained for each temperature from the operating range. It is done without changes in the network structure and without the need for time consuming and complex measurements and optimiztions.