Neural network modeling of microwave FETs based on third-order distortion characterization

Abstract

A new method for characterization of HEMT distortion parameters, which extracts the coefficents of a Taylor series expansion of Ids(Vgs, Vds), including all cross-terms, is developed from low-frequency harmonic measurements. The extracted parameters will be used either in a Volterra series model around a fixed bias point for 3rd-order characterization of small-signal Ids nonlinearity, or in a large-signal model of Ids characteristic, where its partial derivatives are locally characterized up to the 3rd order in the whole bias region, using a novel neural-network representation. The two models are verified by one-tone and two-tone intermodulation distortion (IMD) tests on a PHEMT device

Experimental insight into the third‐order intercepts and nonlinear distortion of GaN HEMTs

“…Because the bias level affects the TOI, the channel‐to‐Schottky junction can be rectified if the bias voltage and the saturation current are very large. So, the best biasing choice for minimal distortion is when the gain of the device is maximal 34 . The gate dimensions often play a significant role leading to TOI magnitude with short gate length L g and wide gate width W g .…”

“…So, the best biasing choice for minimal distortion is when the gain of the device is maximal. 34 The gate dimensions often play a significant role leading to TOI magnitude with short gate length L g and wide gate width W g . However, a compromise must be made as per the appropriate specification, because the carrier density of the 2-DEG is influenced by large W g .…”

Signal-noise support vector model of a microwave transistor

“…Substituting a i for ðâ i À a i Þ and taking into account that a i Àâ i ¼ 0, the (dual space) objective function (10) given in the input space x can be rewritten in the F-feature space as follows:…”

“…Neural transistors [4][5][6][7] are typical black-box models of microwave transistor that generalizes the signal-and noise parameters from N-measured samples accurately over the whole operation domain of the device, which consists of bias conditions (V DS , I DS ) and the operation frequency f. These black-box models are employed successfully in designing microwave devices and circuits [8,9]. Neural networks are also utilized in characterization of the nonlinear properties of the device such as third order distortion characterization of a transistor in [10].…”

Advanced neural network modeling techniques for efficient CAD of microwave filters

“…As a result the circuit optimization using neural models becomes advantageous and computationally inexpensive. For these reasons, it has gained popularity in RF/microwave computer aided design area and has been proven to be very useful for modeling passive microwave structures and components [4], [36]- [37], library of model [38], vias and interconnects [39], coplanar wave-guide components [40], transistor modeling [41]- [43], noise modeling [44]- [46], electromagnetic CAD [47]-12 [49], integrated circuits [50], [51], amplifiers [52], [53], microwave filters [7]- [10], [54], microwave optimization [55], [56], loaded cylindrical cavity [57], shielded microwave circuit [58], frequency selective surface [59], etc.…”