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In this research, four different equivalent circuit models that characterize the substrate/buffer loading effect for GaN high electron mobility transistor (HEMT) on Si substrate have been investigated. Y‐parameter measurements of an open de‐embedded structure have been used to characterize this effect in GaN HEMT on Si. An optimization technique based on hybridization between two meta‐heuristic techniques, which are particle swarm optimization (PSO) and grey wolf optimization (GWO) has been developed to extract physical relevant model elements. The proposed optimization technique is applied on GaN HEMTs of different sizes and validated by means of S‐parameters fitting. In addition, the extracted elements are evaluated in terms of their reliability and scalability. The proposed PSO‐GWO technique shows a very good agreement with previous research results. It has been found that the circuit topology of both resistive and capacitive shunt branches is more accurate in characterizing conductive and displacement substrate/buffer leakage current, respectively. The results of this research validate the applicability of the proposed technique and the model topology for small and large‐signal modeling of GaN transistors on Silicon substrate. [ABSTRACT FROM AUTHOR]

In this paper, a comparative analysis has been conducted on GaN high electron mobility transistor (HEMT) technology on Si and SiC substrates. Small‐signal characteristics of 2‐mm GaN‐on‐Si and GaN‐on‐SiC devices have been investigated. Both devices have the same gate length of 0.5 μm. Special emphasis has been put on the temperature dependence of the buffer/substrate loading effects arising from the Si substrate. As a matter of fact, the "cold" pinch‐off admittance (Y‐) parameter measurement showed significant loading effect for the Si‐based device with respect to the SiC‐based one. This has been clearly supported by the analysis of the extracted parameters of the small‐signal equivalent‐circuit model. The model was also validated by simulating active scattering (S‐) parameters, which showed a very good agreement with the corresponding measurements. The results of this paper highlight the impact of buffer/substrate leakage currents on small‐signal characteristics and the importance of taking this into account during the modeling phase of the GaN‐on‐Si HEMT technology. The lower thermal conductivity of this substrate increases the internal temperature, thus stimulating more leakage and reduction of the device power efficiency. [ABSTRACT FROM AUTHOR]

This paper explores and develops efficient temperature‐dependent small‐signal modeling approaches for GaN high electron mobility transistors (HEMTs). The multilayer perceptron (MLP) architecture and cascaded MLP architecture of artificial neural network are employed to model temperature dependence of 2‐mm GaN‐on‐silicon device. It is identified that both architectures face problem of dependence on initials values of weights and biases. To overcome this issue, the genetic algorithm (GA) is incorporated in both MLP and cascaded MLP architectures. The models are trained on a large set of operating conditions (bias voltages and ambient temperatures) over a frequency range of 0.1 to 20 GHz and then tested for both temperature interpolation and extrapolation cases to assess their accuracy and robustness. An excellent agreement between the measured and the modeled S‐parameters over the entire frequency range demonstrate the quality and robustness of the proposed technique. It is also shown that the cascaded MLP with GA exhibits better performance but with increased complexity. [ABSTRACT FROM AUTHOR]

Based on the earlier experimental investigation of the existing GaAs pHEMT small‐signal modeling approaches and their applicability to different manufacturing processes, a combined automatic small‐signal noise model extraction technique, suitable for design of low‐noise and buffer amplifiers is proposed. The technique is based on the usage of measured S‐parameters of passive test structures and S‐parameters of the transistor in cold modes. Expressions are given for extraction of the intrinsic parameters of an equivalent circuit using linear regression. It is shown that the application of the proposed method allows extracting a small‐signal GaAs pHEMT model both in the probe‐tip reference planes and at on‐wafer calibration planes. The moving average algorithm was applied for preprocessing the results of measurements of the 50 Ohm noise figure during extraction of the noise model. The results of S‐parameters and noise figure simulation agree well with the measurements. The new technique was implemented as a plugin in a commercial EDA tool and enables to derive a ready‐to use small‐signal noise model from measured S‐parameters and 50 Ohm noise figure of a 0.15 μm GaAs pHEMT. [ABSTRACT FROM AUTHOR]

As the cutoff frequency of InP HEMTs enters the terahertz band, high frequency measurement and modeling techniques in hundreds of gigahertz become urgent needs for further millimeter monolithic integrated circuits design. We proposed a new de‐embedding method linking device measurements and modeling based on full EM simulation data acquired from HFSS and advanced design system (ADS). The simulation results for passive dummy structures are well consistent with experiments, and the de‐embedding method is proved very effective for a resistive passive device with high distributed embedding surroundings in frequency range below 40 GHz. Based on these experimental facts, the EM simulations were extended up to 300 GHz and corresponding de‐embedding deviation was further investigated. Results show that the proposed de‐embedding method has very high accuracy in the whole frequency region with a maximum S‐parameters deviation of only 2.58%. However, further analysis proves that the small residual errors still significantly affect extracted small signal model parameters of InP HEMTs especially for transit time τ. Thus, further improvements on de‐embedding accuracy or careful considerations of more error functions in modeling process are necessary for obtaining physically meaningful model parameters. [ABSTRACT FROM AUTHOR]

Abstract: A new modeling methodology for gallium nitride (GaN) high‐electron‐mobility transistors (HEMTs) based on Bayesian inference theory, a core method of machine learning, is presented in this article. Gaussian distribution kernel functions are utilized for the Bayesian‐based modeling technique. A new small‐signal model of a GaN HEMT device is proposed based on combining a machine learning technique with a conventional equivalent circuit model topology. This new modeling approach takes advantage of machine learning methods while retaining the physical interpretation inherent in the equivalent circuit topology. The new small‐signal model is tested and validated in this article, and excellent agreement is obtained between the extracted model and the experimental data in the form of dc I–V curves and S‐parameters. This verification is carried out on an 8 × 125 μm GaN HEMT with a 0.25 μm gate feature size, over a wide range of operating conditions. The dc I–V curves from an artificial neural network (ANN) model are also provided and compared with the proposed new model, with the latter displaying a more accurate prediction benefiting, in particular, from the absence of overfitting that may be observed in the ANN‐derived I–V curves. [ABSTRACT FROM AUTHOR]

To capture variations in the Si/SiGe HBT process characteristics, we could extract a complete equivalent-circuit model for each device, but this would be a time-consuming process. In this article, we develop an alternative approach based on an artificial neural network (ANN). To keep the complexity of the ANN low, we limit this mapping to the most sensitive elements by utilizing sensitivity analysis on a reference device. The results show that the ANN predicts the model parameters very well. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005. [ABSTRACT FROM AUTHOR]

This article presents an artificial neural network (ANN) approaches for small‐ and large‐signal modeling of active devices. The small‐signal characteristics were modeled by S‐parameters based feedforward NN models. The models have been implemented to simulate the bias, frequency and temperature dependence of measured S‐parameters. Feedback NN based large‐signal model was developed and implemented to simulate the drain current and its inherent thermal effect due to self‐heating and ambient temperature. Both small‐ and large‐signal models have been validated by measurements for 100‐μm and 1‐mm GaN high electron mobility transistors and very good agreement was obtained. [ABSTRACT FROM AUTHOR]