A complete methodology for assessing GaN behaviour for military applications

Moreau, Christian; Pipec, M. Le; Tence, S.; Hemery, J.Y.; Rigo, J.; Guérin, C.; Ruelloux, D.; Schneider, C.; Helleye, P. Le

doi:10.1016/j.microrel.2010.07.093

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…[1][2][3] GaN outperforms conventional silicon in terms of exceptionally high electron mobility, high break down electric field intensity, high thermal conductivity and wide band gap range, making them attractive for widespread applications ranging from the advanced high frequency power devices to components used in aerospace and military scenarios. [4][5][6][7][8][9] GaN crystals can be grown on kinds of substrates, including sapphire, silicon carbide (SiC) and silicon (Si). Growing GaN defects but also possible defects in inner dielectric layer and buffer layer.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Physicochemical Mechanism for Negative Bias Temperature Instability in GaN‐HEMTs by Extracting Activation Energy of Dislocations

Chang

Wang

Zhou

et al. 2022

Adv Materials Inter

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epitaxial layers on silicon enables the use of existing silicon manufacturing infrastructure, eliminating the need for costly specific production facilities and leveraging large diameter silicon wafers at low cost. [10,11] Although silicon is a relatively cheap substrate compared with other substrate candidates, but has some distinct disadvantages since silicon and GaN are unmatched material systems. [12] The discrepancies in crystalline structure of GaN and silicon leads to higher lattice mismatches, behaving like dislocations and other types of defects. [13][14][15][16] The crystal defects affect the device performance and reliability to a large extent. A typical conundrum in GaN filed is related to the fact that the dislocations/lattice mismatch could exert inevitable negative effects on the device's basic performance, stability, and reliability. [17][18][19][20] Intensive researches indicated that charge trapping process caused by defects significantly affect the device performance. [21][22][23][24][25][26][27] As the most promising power device, gallium nitride (GaN)-based high electron mobility transistors (HEMTs) typically works under high voltage over a prolonged period. The long-term accumulation of heat during the operation appears to be another unfavorable factor that deteriorates the reliability. [28][29][30][31] The general degradation of electrical performance under negative bias condition at elevated temperatures is collectively known as negative bias temperature instability (NBTI) problem. [32] Although the NBTI problem was first noticed and mainly discussed in p-MOSFET devices, [33][34][35][36] it also perplexes depletion-mode or normally-ON GaN-HEMTs which is the most frequently used GaN device. [37][38][39][40][41][42][43][44] NBTI-related issues in depletion-mode GaN-HEMTs with different kinds of dielectric layer have been investigated in a series of researches. [41,[45][46][47][48][49][50] The NBTI-induced reliability issues in GaN-HEMTs have been acknowledged as a major concern that contributes to the operational instability. While intensive efforts have been made to optimize it from the perspective of manufacturing process or selected materials, [45,51,52] insufficient attention has been devoted to fundamental understanding of the intrinsic mechanisms. Some studies [41][42][43][44][45][46] attributed the degradation to the charge and discharge process of defects at the interface of GaN buffer layer and dielectric layer, while others [47][48][49][50] considered the degradation cause is not only the interface

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Section: Introductionmentioning

confidence: 99%

Exploration of Physicochemical Mechanism for Negative Bias Temperature Instability in GaN‐HEMTs by Extracting Activation Energy of Dislocations

Chang

Wang

Zhou

et al. 2022

Adv Materials Inter

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“…The area of mechanical properties of materials, e.g., hardness and elastic moduli, is of major importance given the significant rise in the number of applications of these properties in diverse areas such as: Health science [1][2][3][4][5][6][7]; metal-mechanical industry [7][8][9][10]; elastomers [11]; military technology [12][13]; and oil and gas prospection [14][15]. Materials surface and thin films mechanical properties evaluation was very difficult in the past, mostly due to the lack of suitable techniques.…”

Section: Introductionmentioning

confidence: 99%

Elasto – Plastic materials behavior evaluation according to different models applied in indentation hardness tests

2019

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Improved Verilog‐A Based Artificial Neural Network Modeling Applied to GaN HEMTs

Jarndal,

Ansari,

Dautov

et al. 2024

Advcd Theory and Sims

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This study presents a novel approach to implementing an artificial neural network (ANN) model for simulating high electron mobility transistors (HEMTs) in Keysight ADS through integrating Verilog‐A coding. It streamlines the realization of ANN models characterized by diverse complexities and layer structures. The proposed method is demonstrated by developing nonlinear models for GaN HEMT on two distinct substrates. GaN‐on‐Si and GaN‐on‐SiC with respective and gate widths are characterized by S‐parameters at a grid of gate and drain bias conditions. The intrinsic gate capacitance and conductances are extracted from the de‐embedded S‐parameters, which are then integrated to find the gate charges and currents. The drain current with the inherent self‐heating and trapping effects is modeled based on the pulsed IV measurement at well‐defined quiescent voltages. Subsequently, the related ANN models of these nonlinear elements are interconnected to form the intrinsic part of the large‐signal model. This intrinsic part with all ANN sub‐models is then completely implemented using a Verilog‐A‐based code. The whole ANN large‐signal model is then validated by single‐ and two‐tone radio frequency large‐signal measurements, which shows a perfect fitting with a high convergence rate. The overall simulation time is five times reduced when the developed Verilog‐A‐based ANN is used instead of the table‐based model. Overall, the large‐signal Verilog‐A‐based ANN model exhibits an improved performance enhancement compared to the conventional table‐based models. This indicates the practical viability of the Verilog‐A integration technique in modeling the nonlinear GaN HEMTs.

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A complete methodology for assessing GaN behaviour for military applications

Cited by 4 publications

References 17 publications

Exploration of Physicochemical Mechanism for Negative Bias Temperature Instability in GaN‐HEMTs by Extracting Activation Energy of Dislocations

Exploration of Physicochemical Mechanism for Negative Bias Temperature Instability in GaN‐HEMTs by Extracting Activation Energy of Dislocations

Elasto – Plastic materials behavior evaluation according to different models applied in indentation hardness tests

Improved Verilog‐A Based Artificial Neural Network Modeling Applied to GaN HEMTs

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