Neural networks in microwave low-noise transistor modeling under various temperature conditions

The paper considers neural network based modeling of the temperature dependence of low-noise microwave transistor signal and the noise performance. Two approaches are proposed. One is a combination of a transistor empirical model and neural networks and the other is completely based on neural networks. Implementation of the proposed models into a microwave simulator is described. For a specified component, comparison results are given of signal and noise parameter prediction using these models under various temperature conditions.