Fet Small Signal Modelling Based on the DST and Mel Frequency Cepstral Coefficients

Abstract

Abstract-In this paper, a new technique is proposed for field effect transistor (FET) small-signal modeling using neural networks. This technique is based on the combination of the Mel frequency cepstral coefficients (MFCCs) and discrete sine transform (DST) of the inputs to the neural networks. The input data sets to traditional neural systems for FET small-signal modeling are the scattering parameters and corresponding frequencies in a certain band, and the outputs are the circuit elements. In the proposed approach, these data sets are considered as forming random signals. The MFCCs of the random signals are used to generate a small number of features characterizing the signals. In addition, other MFCCs vectors are calculated from the DST of the random signals and appended to the MFCCs vectors calculated from the signals. The new feature vectors are used to train the neural networks. The objective of using these new vectors is to characterize the random input sequences with much more features to be robust against measurement errors. There are two benefits for this approach: a reduction in the number of neural networks inputs and hence a faster convergence of the neural training algorithm and robustness against measurement errors in the testing phase. Experimental results show that the proposed technique is less sensitive to measurement errors than using the actual measured scattering parameters.

NONLINEAR MODELING OF TRAPPING AND THERMAL EFFECTS ON GaAs AND GaN MESFET/HEMT DEVICES

“…As mentioned above, the effects of ambient temperature are introduced into the DC model for I ds using expression (6). The effects of selfheating are incorporated in the model in an implicit manner.…”

“…The simulation of the DC and dynamic characteristics of MESFET and HEMT devices as a function of bias (whether they be static and/or dynamic), temperature and frequency has received much attention over recent years [1][2][3][4][5][6][7][8][9][10][11]. Addressing these three areas with a nonlinear model which provides a good compromise between accuracy, model complexity, and ease of parameter extraction has proved to be troublesome.…”