Equivalent small-signal model of InP-based HEMTs with accurate radiation effects characterization

Abstract

In this paper, an effective equivalent modeling technique has been proposed to describe small-signal characteristics of InP-based high electron mobility transistors (HEMTs) after proton radiation, which is composed of an artificial neural network and equivalent-circuit models. Small-signal intrinsic parameters of InP-based HEMTs are extracted from S-parameters before and after 2 MeV proton radiation as modeling objects. The deep learning model of a generative adversarial network has been explored to expand the measured finite data samples. Four feedforward neural networks are incorporated to equivalent-circuit topology to form the equivalent model, which are trained to accurately predict the radiation-induced variations of Cgs, Cgd, Rds, and gm, respectively. The prediction accuracy of the developed equivalent model has been well verified in terms of the broad-band S-parameters under radiation fluence of 1 × 1014 and 5 × 1013 H+/cm2. This equivalent modeling method with characterization of radiation damage effects could provide significant guidance for the aerospace monolithic millimeter-wave integrated circuit design.