Machine learning technique based extremely broadband small‐signal behavioral model for InP DHBTs

A novel modeling methodology for indium phosphide (InP) double heterojunction bipolar transistors (DHBTs) based on the theory of Bayesian inference, a well‐known method from the field of machine learning, is presented in this article. An extremely broadband small‐signal behavioral model, from 200 MHz to 325 GHz, is built, tested, and validated in this work, with excellent agreement obtained between the extracted model and the experimental data in the form of S‐parameters. A single finger InP DHBT device, with emitter size of 0.5 × 5 μm² exhibiting an ft of over 550 GHz, is used in the verification example. Taking advantage of regression techniques based on machine learning concepts, the proposed black‐box behavioral model can more accurately predict the behavior of the device compared with the traditional equivalent circuit modeling method. Several sets of measured vs modeled data are shown, indicating the efficacy of the method.     

New small‐signal extraction method applied to GaN HEMTs on different substrates

In this article, a new extraction technique is proposed to extract the small‐signal parameters of gallium nitride (GaN) high electron mobility transistors (HEMTs) on three different substrates namely, Si, SiC, and Diamond. This extraction technique used a single small‐signal circuit model to efficiently describe the physical and electrical properties of GaN on different substrates. This technique takes into account any asymmetry between the gate‐source and gate‐drain capacitances on the asymmetrical GaN HEMT structure, charge‐trapping effects, passivation layer inclusion, as well as leakage currents associated with the nucleation layer between the GaN buffer layer and the different substrates. The extracted values were then optimized using the grey wolf optimizer. The proposed technique was demonstrated through a close agreement between simulated and measured S‐parameters.     

Large‐signal modeling methodology for GaN HEMTs for RF switching‐mode power amplifiers design

A large‐signal model for GaN HEMT transistor suitable for designing radio frequency power amplifiers (PAs) is presented along with its parameters extraction procedure. This model is relatively easy to construct and implement in CAD software since it requires only DC and S‐parameter measurements. The modeling procedure was applied to a 4‐W packaged GaN‐on‐Si HEMT, and the developed model is validated by comparing its small‐ and large‐signal simulation to measured data. The model has been employed for designing a switching‐mode inverse class‐F PA. Very good agreement between the amplifier simulation and measurement shows the validity of the model. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

An improved millimeter‐wave small‐signal modeling approach for HEMTs

An improved method to determine the small‐signal equivalent circuit model for HEMTs is presented in this study, which is combination of the analytical approach and empirical optimization procedure. The parasitic inductances and resistances are extracted under pinch‐off condition. The initial intrinsic elements are determined by conventional analytical method. Advanced design system (agilent commercial circuit simulator) is used to optimize the whole model parameters with small deviation of initial values. An excellent agreement between measured and simulated S‐parameters is obtained for 2 × 20 μm² gate width HEMT up to 40 GHz. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:464–469, 2014.     

Hybrid small‐signal model parameter extraction of GaN HEMTs on Si and SiC substrates based on global optimization

This article presents efficient parameters extraction procedure applied to GaN High electron mobility transistor (HEMT) on Si and SiC substrates. The method depends on combined technique of direct and optimization‐based to extract the elements of small‐signal equivalent circuit model (SSECM) for GaN‐on‐Si HEMT. The same model has been also applied to GaN‐on‐SiC substrate to evaluate the effect of the substrates on the model parameters. The quality of extraction was evaluated by means of S‐parameter fitting at pinch‐off and active bias conditions.     

An improved empirical large‐signal model for the GaAs‐ and GaN‐based HEMTs

A complete empirical large‐signal model for the GaAs‐ and GaN‐based HEMTs is presented. Three generalized drain current I–V models characterized by the multi‐bias Pulsed I–V measurements are presented along with their dependence on temperature and quiescent bias state. The new I–V equations dedicated for different modeling cases are kept accurate enough to the higher‐order derivatives of drain‐current. Besides, an improved charge‐conservative gate charge Q–V formulation is proposed to extract and model the nonlinear gate capacitances. The composite nonlinear model is shown to accurately predict the S‐parameters, large‐signal power performances as well as the two‐tone intermodulation distortion products for various types of GaAs and GaN HEMTs. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2011.     

Differential evolution based small signal modeling of GaN HEMT

In this article differential evolution based method of small signal modeling of GAN HEMT has been investigated. The method uses a unique search space exploration strategy to obtain optimized values of intrinsic and extrinsic elements pertaining to compact small signal model from extracted equivalent circuit elements and measured S‐parameter data. Effectiveness of the method has been illustrated by comparing the measured S‐parameter data of a 4 × 0.1 × 75 μm² GaN/SiC HEMT in the frequency range of 1 to 30 GHz wherein modeled and measured data are in good agreement.     

Microwave frequency small‐signal equivalent circuit parameter extraction for AlInN/GaN MOSHEMT

This article presents an accurate and efficient extraction procedure for microwave frequency small‐signal equivalent circuit parameters of AlInN/GaN metal‐oxide‐semiconductor high electron mobility transistor (MOSHEMT). The parameter extraction technique is based on the combination of conventional and optimization methods using the computer‐aided modeling approach. The S‐, Y‐, and Z‐ parameters of the model are extracted from extensive dynamic AC simulation of the proposed device. From the extracted Y‐ and Z‐ parameters the pad capacitances, parasitic inductances and resistances are extracted by operating the device at low and high frequency pinch‐off condition depending upon requirement. Then, the intrinsic elements are extracted quasi analytically by de‐embedding the extrinsic parameters. S‐parameter simulation of the developed small‐signal equivalent circuit model is carried out and is compared with TCAD device simulation results to validate the model. The gradient based optimization approach is used to optimize the small‐signal parameters to minimize the error between developed SSEC model and device simulation based s‐parameters. The microwave characteristics of optimized SSEC model is carried out (f_T = 169 GHz and f_max = 182 GHz) and compared with experimental data available from literature to validate the model.     

Genetic algorithm‐based neural‐network modeling approach applied to AlGaN/GaN devices

An accurate equivalent circuit large‐signal model (ECLSM) for AlGaN‐GaN high electron mobility transistor (HEMT) is presented. The model is derived from a distributed small‐signal model that efficiently describes the physics of the device. A genetic neural‐network‐based model for the gate and drain currents and charges is presented along with its parameters extraction procedure. This model is embedded in the ECLSM, which is then implemented in CAD software and validated by pulsed and continuous large‐signal measurements of on‐wafer 8 × 125‐μm GaN on SiC substrate HEMT. Pulsed IV simulations show that the model can efficiently describe the bias dependency of trapping and self‐heating effects. Single‐ and two‐tone simulation results show that the model can accurately predict the output power and its harmonics and the associated intermodulation distortion (IMD) under different input‐power and bias conditions. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Bias‐dependent small‐signal modeling based on neuro‐space mapping for MOSFET

In this article, bias‐dependent small‐signal modeling approach based on neuro‐space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100 MHz–40 GHz confirming the validity and effectiveness of our approach. In addition, higher accuracy is achieved by our approach in contrast to conventional empirical model. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

AlGaN/GaN HEMTs on SiC and Si substrates: A review from the small‐signal‐modeling's perspective

In this article, small‐signal modeling approaches for GaN HEMTs on SiC and Si substrates have been developed. The main advantage of these approaches is their accuracy, reliability, and dependency on only cold S‐parameter measurements to extract the parasitic elements of the device. The proposed equivalent circuit model for GaN on Si HEMT considers extra effects due to parasitic conduction through substrate or buffer layers. S‐parameter measurements at different bias conditions in addition to physical based analysis have been used to validate the accuracy and reliability of the developed modeling methods. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:389–400, 2014.     

Neural‐space mapping‐based large‐signal modeling for MOSFET

In this article, a large‐signal modeling approach based on the combination of equivalent circuit and neuro‐space mapping modeling techniques is proposed for MOSFET. In order to account for the dispersion effects, two neuro‐space (S) mapping based models are used to model the drain current at DC and RF conditions, respectively. Corresponding training process in our approach is also presented. Good agreement is obtained between the model and data of the DC, S parameter, and harmonic performance for a 0.13 μm channel length, 5 μm channel width per finger and 20 fingers MOSFET over a wide range of bias points, demonstrating the proposed model is valid for DC, small‐signal and nonlinear operation. Comparison of DC, S‐parameter, and harmonic performance between proposed model and empirical model further reveals the better accuracy of the proposed model. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2011.     

Microwave analysis of SiGe heterojunction double‐gate tunneling field‐effect transistor through its small‐signal equivalent circuit

In this study, Si_0.5Ge_0.5 was used as a source junction material in a tunneling field‐effect transistor (TFET), which was analyzed using technology computer‐aided design (TCAD) simulation and a small‐signal non‐quasi static (NQS) equivalent circuit. The NQS equivalent circuit with additional tunneling resistance (R_tunnel) enables more accurate analyses. By using a de‐embedding process, small‐signal parameters in the intrinsic area were obtained. This process was used to analyze the resistance and capacitance in each section, the tendencies of the materials, and the voltage. The error between the NQS equivalent circuit and TCAD device simulation was within 1.9% in the 400‐GHz regime. A cut‐off frequency (f_T) of up to 0.876 GHz and maximum oscillation frequency (f_max) of 146 GHz were obtained.     

A combined technique for amplifier oriented small‐signal noise model extraction

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.     

In‐deep insight into the extrinsic capacitance impact on GaN HEMT modeling at millimeter‐wave band

The extrinsic input and output capacitances of the field effect transistor small‐signal equivalent circuit are typically extracted from the low frequency admittance parameters under “cold” pinch‐off condition. Despite that, these two capacitances play a significant role also at high frequencies. Intuitively, a first hint of explanation stems from the high frequency reduction of their admittance values connected in parallel to the input and the output of the rest of the equivalent circuit. In particular, the extrinsic capacitances can cause an increase of the real parts of the impedance parameters at high frequencies. This article is aimed at developing an extensive experimental and mathematical analysis based on a comparative study of this behavior for GaN high electron mobility transistor (HEMT) devices up to the millimeter‐wave range. The results of this analysis can be applied for estimating the extrinsic capacitances. The main benefit of this modeling technique is that the extrinsic output capacitance can be separated from the intrinsic output capacitance, which can play a significant role especially in case of large devices. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2012.     

Multibias and temperature dependence of the current‐gain peak in GaN HEMT

Thermal and multibias behavior of the peak in the short‐circuit current‐gain (h₂₁) has been investigated for a GaN HEMT, aiming to contribute to an extensive knowledge on it. To obtain a simple and complete insight of this phenomenon and its influence in device performance over operating conditions, high‐frequency multibias scattering (S‐) parameter measurements have been analyzed from low to high temperature. It has been observed that the current‐gain peak might get to be more or less serious depending on the working circumstances. The peak affecting h₂₁ has been successfully reproduced by using an equivalent‐circuit model. Moreover, a novel procedure has been developed to interpret this kind of phenomenon by quantifying the area of the current‐gain peak (ACGP), which is denoted as the area corresponding to h₂₁ curves with and without the peak. It is found that the ACGP is strongly dependent on bias and less dependent on temperature. The relevance of a comprehensive evaluation of the peak in h₂₁ lies in its usefulness for empowering RF engineers to efficiently consider it for both device modeling and circuit design.     

Complete parasitic‐capacitance‐shell extraction of high‐frequency switch‐HEMT equivalent‐circuit model

This paper presents a new solution to a particular problem of high electron‐mobility transistor (HEMT) equivalent‐circuit modeling, that is, complete parasitic‐capacitance‐shell extraction of high‐frequency single‐gate and dual‐gate switch‐based HEMTs, which is very important to the accuracy of high‐frequency HEMT switch models, but not important in the conventional common‐source HEMT modeling for amplifier‐applications. A full‐wave electromagnetic (EM) analysis based method is proposed to analytically extract the complete parasitic‐capacitance‐shell of single‐gate and dual‐gate switch‐based HEMTs. All the 6 parasitic capacitances of the single‐gate switch‐based HEMT and all the 10 parasitic capacitances of the dual‐gate switch‐based HEMT are extracted by linear equations. No resistance parameter is needed to calculate the capacitance‐to‐ground and the interelectrode‐capacitance, and for the first time, all the 10 parasitic capacitances of the dual‐gate switch‐based HEMT are completely considered and analytically extracted. Then, a consistent and systematic modeling procedure of single‐gate and dual‐gate switch‐based HEMT is verified. With the complete parasitic‐capacitance‐shells extracted, the accurate intrinsic model of the single‐gate HEMT can be directly embedded into the parasitic‐shell of the dual‐gate HEMT. The predicted scattering parameters of the single‐gate and dual‐gate series switches fit well with the measurements up to 40 GHz, and accurate linear scalability are also found.     

A HEMT large‐signal model for use in the design of microwave switching circuits

The nonlinear sources of switch‐HEMTs have been well analyzed by using the measured data. The small signal intrinsic capacitances (under both positive and negative V _ds operation) have been extracted by an extended small signal model. one‐dimension capacitance model has been effectively applied to model the small signal incremental capacitances directly extracted from the key operation region, which has also automatically taken into account the surface trapping effects. A new capacitance model has been effectively proposed to well fit the key nonlinear source (the deep subthreshold capacitance) of switch‐HEMTs. Simple switching function and additional voltage dependence have been applied to model the wide linear‐region (from high‐ V _gs region to deep subthreshold region) of channel current. On/off state small signal insertion loss, small signal isolation, weak harmonics, and power carrying capabilities are accurately predicted by the large signal model. The model shows very good convergence of circuit simulation. Meanwhile, the simple equations and distinguishing among the capacitances accurately make the scaling rules simple and accurate.     

Fuzzy neural‐based approaches for efficient RF/microwave transistor modeling

In today's RF and microwave circuits, there is an ever‐increasing demand for higher level of system integration that leads to massive computational tasks during simulation, optimization, and statistical analyses, requiring efficient modeling methods so that the whole process can be achieved reliably. Since active devices such as transistors are the core of modern RF/microwave systems, the way they are modeled in terms of accuracy and flexibility will critically influence the system design, and thus, the overall system performance. In this article, the authors present neural‐ and fuzzy neural‐based computer‐aided design techniques that can efficiently characterize and model RF/microwave transistors such as field‐effect transistors and heterojunction bipolar transistors. The proposed techniques based on multilayer perceptrons neural networks and c‐means clustering algorithms are demonstrated through examples. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

Direct extraction method of HEMT switch small‐signal model with multiparasitic capacitive current path

It has been found that the analytical extraction methods cannot be applied to the usual test structure of the switch high electron‐mobility transistor (HEMT) with a large‐value gate grounded resistor. The significant effect of the precise multicapacitive current path on switch model precision has also been found. The multicapacitive current path here is different from the seemingly similar hypothesis proposed for the distributed parasitic effects at high frequencies (eg, D‐band). In fact, for switch based HEMT, it is important to distinguish between the capacitive current paths accurately even at relatively low frequencies. Due to the existing of the large gate resistance, the usual capacitance mix decreases the accuracy of the switch model significantly. Thus an analytical method has been developed to calculate parasitic capacitances (the capacitance to ground and the interelectrode capacitance) through full‐wave electromagnetic analysis. For practical applications and further verification, the whole HEMT switch small‐signal models and the direct extraction methods are presented. The simulated results fit well with the measurements up to 40 GHz.