A Quasi-Physical Compact Large-Signal Model for AlGaN/GaN HEMTs

Wen, Zhang; Xu, Yuehang; Tao, Hongqi; Ren, C.; Lu, Haijun; Wang, Zhensheng; Zheng, Wei; Zhang, Bin; Chen, Tangsheng; Gao, Tao; Xu, Ruimin

doi:10.1109/tmtt.2017.2765326

Cited by 52 publications

(43 citation statements)

References 47 publications

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“…GaN-based high electron mobility transistors (HEMTs) have been widely used for RF power electronic applications, benefitting from the properties of high power density and high frequency. [1][2][3] Although GaN HEMTs have been demonstrated to exceed 40 W/mm of RF power density, the typically operated power density is 5 to 7 W/mm, 4 which is due to the self-heating effects. Self-heating leads to increase channel temperature, presenting a significant influence on the device reliability and performance.…”

Section: Introductionmentioning

confidence: 99%

Temperature‐dependent small signal performance of GaN‐on‐diamond HEMTs

Zhou²,

Kong³

et al. 2019

Int J Numerical Modelling

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This paper presents the temperature dependence of small signal performance of GaN-on-diamond high electron mobility transistors (HEMTs) at an ambient temperature range from 0°C to 125°C. The temperature influence on the parasitic resistances together with the intrinsic parameters is investigated, and the temperature coefficients of these parameters are extracted from measured data. For comparison, a GaN-on-SiC device is also investigated. These results are important for the development and application of the GaN-on-diamond HEMT technology. KEYWORDSGaN-on-diamond, high electron mobility transistor (HEMT), small signal parameters, temperature dependence

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Section: Introductionmentioning

confidence: 99%

Temperature‐dependent small signal performance of GaN‐on‐diamond HEMTs

Zhou²,

Kong³

et al. 2019

Int J Numerical Modelling

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“…Meanwhile, GaN monolithic microwave integrated circuit (MMIC) products have reached a maximum operation frequency of W-band. 4 A wideband and accurate small-signal equivalent circuit model (SSECM) is the foundation of large-signal modeling in bottom-up method 5,6 ; meanwhile, it can also be used as a cornerstone for constructing noise models. 7,8 Furthermore, the small signal parameters can provide an essential feedback for device manufactures.…”

Section: Introductionmentioning

confidence: 99%

A reliable and efficient small‐signal parameter extraction method for GaN HEMTs

Yong-bo

Luo³

et al. 2018

Int J Numerical Modelling

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In this paper, a reliable and efficient parameter extraction method for GaN high electron mobility transistor (HEMT) small-signal models has been proposed. By utilizing parameter scanning method, the initial values of the extrinsic elements are first extracted from the cold pinch-off and cold unbiased measurement conditions, respectively. An iterative optimization algorithm is employed then to optimize the extrinsic elements. This scanning and iteration combined algorithm based on a direct extraction method of extrinsic elements can reduce the impact of the approximation error, which makes the determined values of the small-signal parameters more reliable. The extraction flow has been realized in a Matlab program for efficiency. A 16-element small-signal equivalent circuit model (SSECM) for GaN HEMTs has been employed, and the new parameter extraction method has been validated by comparing the simulated small-signal S-parameters with the measured data from 0.1 GHz to 40 GHz for two different device dimensions.

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“…With the development of semiconductor manufacture techniques, developing accurate computer-aided design (CAD) models of large-signal power transistors becomes necessary [1,2]. Accurate large-signal models for the power transistors which can be used in commercial microwave circuit simulators play a decisive role in the circuit/system design [3].…”

Section: Introductionmentioning

confidence: 99%

Accurate large-signal modeling using neuro-space mapping for power transistors

Yan

Jin

Zhang

et al. 2018

IEICE Electron. Express

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Abstract:In this paper, a novel neuro-space mapping (Neuro-SM) modeling approach for lager-signal transistors is proposed. A new structure of Neuro-SM model with capacitors and inductors is created to change the DC and AC characteristic of the model respectively. An additional current signal extracted with a novel nonlinear function is adopted to improve the large-signal characteristic of existing device models while remain the S-parameters unchanged. A step-by-step training method is developed for fast training of the proposed Neuro-SM model avoiding variables adjustment repeatedly. In addition, the modeling experiment for measurement data of LDMOS transistor demonstrate that the novel Neuro-SM method can accurately reflect the large-signal characteristics of transistor with simple operation process and enhance the accuracy of the existing model.

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A Quasi-Physical Compact Large-Signal Model for AlGaN/GaN HEMTs

Cited by 52 publications

References 47 publications

Temperature‐dependent small signal performance of GaN‐on‐diamond HEMTs

Temperature‐dependent small signal performance of GaN‐on‐diamond HEMTs

A reliable and efficient small‐signal parameter extraction method for GaN HEMTs

Accurate large-signal modeling using neuro-space mapping for power transistors

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