A neural network approach for compact cryogenic modelling of HEMTs

This paper reports on a procedure based on the use of artificial neural networks (ANN) to fully model the performance of advanced high electron mobility transistors (HEMT) operating down to cryogenic temperatures. By means of this procedure, we reproduce the DC behaviour and the scattering (S-) parameters of the device under test (DUT). The I-V curves and the S-parameters of the DUT have been compared with measurements, and a good agreement has been found for assessing the capability of the ANN structure to predict the full behaviour of the DUT. Furthermore, we have analysed in detail the performance of two typical parameters of HEMT's, namely the transconductance and the output conductance. Their values have been derived from measured data and have been compared with those obtained by the ANN approach. Both the simulated DC and RF performance have shown an accuracy degree adequate to model the device properties down to cryogenic temperatures.