The developments in AlGaN/GaN heterojunction field effect transistors (HFETs) are beginning to allow harnessing of the great potential of this technology in high-power radio-frequency (RF) applications. However, the integration of HFET into a circuit environment requires accurate small and large signal modeling of the device operating under various biasing conditions. The conventional small signal equivalent circuit modeling methods consist of “cold” measurements for extracting parasitic elements, and on-bias measurements in determining the intrinsic device circuit elements. Also, the optimization routines are often explored directly using the “hot” measurement data to minimize errors. In this paper, an 18-element small signal equivalent circuit model for AlGaN/GaN HFET is proposed and implemented, and contrasted to various de-embedding methods. Among the methods treated, the hot-FET optimization extraction based on the parasitic capacitances obtained from cold measurements leads to the smallest error between the simulated S-parameters and the measured ones at all bias points employed, with an average error of about 5%. This hybrid extraction algorithm is strengthened by imposing constraints to avoid any nonphysical convergence. We note that the extrinsic parasitics determined by this method differ considerably from the values obtained by cold-FET measurements, which implies that the assumption on the bias independency for extrinsic parameters in the latter method might be questionable for AlGaN/GaN HFET.

GaN-based heterojunction field effect transistors (HFETs) will play major roles in the high-power, high-frequency military and commercial arenas for microwave and millimeter wave transmitters and receivers used in communications and radar devices. In fact, devices operative in the X-band (7-12.5 GHz) and beyond are already at market and boast quite impressive performances. Having improved the crystal quality now to levels where the reliability [expressed as mean time to failure (MTTF)] is claimed to exceed ten million hours, the work now needs to focus on which of the physical mechanisms responsible for degradation are the most important, and how the existing degradation accelerates subsequent degradation, ultimately resulting in device failure. Available data show that not all devices from the same wafer show similar longevity and the wide spread of activation energies reported for three-temperature extrapolation-based predictions of the lifetime are troubling. If we hope to make consistent, reliable predictions of device lifetimes, particularly when the devices are being pushed in radio-frequency (RF) operation to near their limits, more work will need to be done in characterizing the long term stability of the devices, and new physical models for the failure mechanisms will have to be developed.