In the emerging 5G and beyond 5G (B5G) era, the spotlight is sharply focused on the power amplifier, a critical component with stringent specification requirements that dictates the performance of the transmitter. The gallium nitride (GaN) device, with its superior inherent properties, is surfacing as a front-runner for power amplifier applications. The increasing demand for high frequency, high linearity, and cost-effective GaN power amplifiers is driven by anticipated traffic surges and the need for extensive 5G deployment. This paper offers a thorough review and future perspective on research developments in RF GaN device technology. It encompasses critical issues in advanced device and circuit technology, with a focus on high frequency, high linearity, cost-effective GaN-on-Si high electron mobility transistors (HEMTs), and compact modeling. This work aims to serve as a guide for the utilization of GaN HEMTs in 5G communication applications. 

A Review of GaN RF Devices and Power Amplifiers for 5G Communication Applications

This paper presents for the first time an accurate ASM-HEMT model for millimeter-wave GaN HEMT technology validated with W-band scalar load-pull and power sweep measurements. The accurate model is used to predict the optimal performance of a GaN HEMT with operating conditions beyond the limitations of the scalar W-band load-pull system. The GaN HEMT measurements exhibits a peak PAE of 35% and the ASMHEMT model predicts a peak PAE of 42%.

Surmounting W-band Scalar Load-Pull Limitations Using the ASM-HEMT Model for Millimeter-Wave GaN HEMT Technology Large-Signal Assessment

In this article, we present a novel deep learning (DL) framework that fully automates the parameter extraction process for the BSIM-CMG unified model for advanced semiconductor devices. The framework seamlessly integrates with the BSIM-CMG model, making it applicable to diverse advanced devices such as GAA nanosheets, nanowire FETs, and FinFETs. Unlike existing approach involving DL for parameter extraction, the proposed framework combines physics-driven parameter initialization and data-driven DL enhancing the computational efficiency and making it easy to implement. It leverages the BSIM-CMG model’s versatility for initial parameter estimation, the efficiency of DL algorithms for model parameter prediction, and the adaptability to various device geometries and configuration. The framework has been successfully validated with extensive numerical simulations and experimental data from 14-nm FinFET device with varying channel widths, 12-nm nanosheet, and 24-nm nanowire FET.

A Novel Physics Aware ANN-Based Framework for BSIM-CMG Model Parameter Extraction

With the development of high-voltage and high-frequency switching circuits, GaN high-electron-mobility transistor (HEMT) devices with high bandwidth, high electron mobility, and high breakdown voltage have become an important research topic in this field. It has been found that GaN HEMT devices have a drift in threshold voltage under the conditions of temperature and gate stress changes. Under high-temperature conditions, the difference in gate contact also causes the threshold voltage to shift. The variation in the threshold voltage affects the stability of the device as well as the overall circuit performance. Therefore, in this paper, a review of previous work is presented. Temperature variation, gate stress variation, and gate contact variation are investigated to analyze the physical mechanisms that generate the threshold voltage (VTH) drift phenomenon in GaN HEMT devices. Finally, improvement methods suitable for GaN HEMT devices under high-temperature and high-voltage conditions are summarized. 

Research on the Reliability of Threshold Voltage Based on GaN High-Electron-Mobility Transistors

We describe the application of Artificial Neural Networks (ANNs) for Gallium Nitride (GaN) High-Electron Mobility Transistor (HEMT) model parameter extraction to improve the model accuracy between 110 and 170 GHz. Fully-connected ANNs trained by backpropagation relate the physics-based ASM-HEMT model parameters to RF transistor measurements. The effects of ANN activation function, number of layers, number of nodes and number of training set data points on training accuracy are studied. For the 12 model parameters that dominate the 40-nm GaN HEMT RF characterization, we obtained a combined root-mean-squared (RMS) error of 2.5% between the ANN prediction and the training set, which is acceptable for most design tasks. 

Artificial Neural Networks for GaN HEMT Model Extraction in D-band Using Sparse Data

In this paper, we validate the industry standard ASM-HEMT model for non-linear large-signal modeling of 140 nm GaN HEMT at X-band. An accurate model has been developed for fundamental, second-, and third-order harmonic frequency. Time-domain waveforms and dynamic load-line simulations from ASM-HEMT model are also validated against non-linear vector analyzer measurements. This is the first validation of ASM-HEMT model for harmonics and NVNA data. A good model agreement with measurements has been obtained.

Accurate non-linear harmonic simulations at X-band using the ASM-HEMT model validated with NVNA measurements

In this paper, a statistical simulation model for variability in I-V characteristics of GaN-HEMTs due to the manufacturing process variations is presented. The variability in I-V characteristics is found to depend on changes in device geometry and device physics parameters caused by manufacturing process variations. This paper presents a systematic methodology to model these variations. Using physics-based formulations of the industry standard ASM-HEMT model and developed methodology, the effects of manufacturing variations on I-V are accurately modeled for the first time. The model has been validated for 114 GaN HEMTs processed at a standard GaN foundry. The developed simulation model is used to analyze the impact of variations on P1dB. This model lays the foundations for yield analysis in GaN HEMT technologies.

Statistical Modeling of Manufacturing Variability in GaN HEMT I-V Characteristics with ASM-HEMT

This paper presents the experimental results from a validation of a newly-developed nonlinear embedding model for the GaN ASM-HEMT model. An extracted ASM-HEMT model was used together with the nonlinear embedding model to synthesize on-wafer Class F operation at 10 GHz for a 150 nm 1W GaN IIEMT. The embedding model provided a set of multi-harmonic terminations at the transistor’s source and load, which were then applied to the physical transistor in an experimental setup. The transistor’s performance with only the predicted load termination at the fundamental is compared with its Class F performance when terminated at the predicted fundamental, second, and third harmonic load terminations, as well as second harmonic source termination. A phase sweep of each harmonic termination is further used to verify that the terminations predicted by the embedding model are optimal. The transistor’s PAE when terminated with the embedding model’s predicted terminations reaches 71%, in dose agreement to the modd’s PAE prediction at peak power.

Experimental Validation of ASM-HEMT Nonlinear Embedding Modeling of GaN HEMTs at X-band

A fast and accurate deep learning (DL) based ASM-HEMT I-V model parameter extraction is presented for the first time. DL-based extraction starts with 120k training data-sets comprising of 374 million I-V data points. Training data-sets are generated through Monte Carlo simulations. The trained DL-model is demonstrated to successfully model 114 GaN HEMTs from a typical GaN fabrication process. The predicted parameters show an excellent fit for the I-V data. In addition, the root-mean-square(RMS) error incurred for key electrical parameters such as pinch-off voltage, linear condition current and the maximum current is 2.2%, 17.6%, and 2.4% respectively. The proposed approach is verified for multiple GaN HEMTs of different sizes. The developed technique can provide a very fast means for parameter extraction with a reasonable accuracy.

Deep Learning-Based ASM-HEMT I-V Parameter Extraction

This paper reports a temperature-dependent ASM-HEMT for modeling GaN HEMTs at elevated temperatures. Modifications to the standard ASM-HEMT were developed to accurately capture the DC and RF measurements collected at varying chuck temperatures. Several results are reported which validate the model including DC-IV, pulsed-IV, scattering-parameter, and load-pull measurements. The model is then used to extrapolate the performance of the GaN HEMT to twice the operating temperature at which the model was validated. This work could be useful for understanding and modeling GaN HEMTs in high-temperature environment applications.

An ASM-HEMT for Large-Signal Modeling of GaN HEMTs in High-Temperature Applications

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