Simulating Noise Performance of Advanced Devices down to Cryogenic Temperatures

Abstract

The aim of the present work is the development of a suitable neural network structure to compute the microwave noise parameters of High Electron Mobility Transistors (HEMT). The noise parameters (NP) here considered are the magnitude (|Γ opt |) and phase (∠Γ opt ) of the optimum noise source reflection coefficient, the minimum noise figure (F min ) and the noise resistance (R n ). By this procedure, we are able to reproduce the above noise parameters of several device types from only one measured scattering parameter set, one frequency point and one generic noise figure value at different bias and/or temperature conditions. The employed noise figure is that measured in input matched conditions (i.e. 50 Ω source impedance), namely F 50 . The trained network outputs were compared with data of noise measurements performed in our lab and the results have shown to be in a very good agreement. We observed that the difference occurring between the expected value of the noise parameters and the value calculated by the neural network is negligible. The procedure here presented is the most effective choice to avoid the need of any circuit simulations. Our neural network allows great time saving, thus leading to a rapid and complete characterization of the active microwave device under test with a remarkable generalization feature.

An evolution algorithm for noise modeling of HEMT’s down to cryogenic temperatures

“…This kind of approach overcomes the difficulties of extracting the values of circuit and noise elements which vary according the device type, bias conditions and temperature. Therefore, application of ANN procedures represents an interesting modeling tool but the accessible generalization features strongly depend on the amount and the choice of data employed in the training phase [8,9].…”

“…This approach is original and very flexible because it does not require a training procedure like in the Artificial Neural Networks (ANNs) -based systems [8,9]. It also allows to perform an analysis of the stability performances of the parameters under test.…”