Degradation Prediction of GaN HEMTs under Hot-Electron Stress Based on ML-TCAD Approach

Wang, Ke; Jiang, Haodong; Liao, Yulin; Xu, Yue; Feng, Yong; Ji, Xiaoli

doi:10.3390/electronics11213582

Cited by 3 publications

(4 citation statements)

References 42 publications

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“…The values of R out at low and high frequency are practically unchanged with varying trap energy, while the frequency at which the transition occurs shifts to lower values with increasing E CT . Notice that larger E CT corresponds to a deeper trap level, hence a lower emission rate (see (7) and ( 8)) and, in general, higher trap occupancy. In order to grasp the mechanism giving rise to this change in the output conductance, we examine the distributed variation source (16) for a trap energy level E CT = 0.46 eV (+10 meV variation with respect to the nominal value).…”

Section: Sensitivity Of Real (Y Dd )mentioning

confidence: 99%

“…We recall that S n T ,0 (r), shown in Figure 7, is the DC component of the trap rate equation residual and essentially corresponds to the net recombination rate (5) at varied energy. Only the generation rate G n explicitly depends on E T through the emission rate (7), and it decreases with growing E T distance from the conduction band. On the contrary, the recombination rate R n is unchanged; hence, the microscopic source essentially equals the variation of the net recombination rate (5), which is, in turn, linearly dependent on the trap concentration.…”

Section: Sensitivity Of Real (Y Dd )mentioning

confidence: 99%

“…On the other hand, the development of GaN technology is still challenging in terms of reliability, cost and material quality. The multiple interfaces and material layers are still characterized by traps and defects [7,8], which ultimately limit the device performance. For example, carbon or iron buffer doping leads to acceptor-type traps [9], responsible for unwanted low-frequency dispersion of the device characteristics due to the slow trap dynamics.…”

Section: Introductionmentioning

confidence: 99%

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TCAD Modeling of GaN HEMT Output Admittance Dispersion through Trap Rate Equation Green’s Functions

2023

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We present a novel and numerically efficient approach to analyse the sensitivity of AC parameters to variations of traps in GaN HEMTs. The approach exploits an in-house TCAD simulator implementing the drift-diffusion model self-consistently coupled with trap rate equations, solved in dynamic conditions with the Harmonic Balance algorithm. The capability of the model is demonstrated studying the low-frequency dispersion of a 150 nm gate-length AlGaN/GaN HEMT output admittance YDD as a function of the trap energy of Fe-induced buffer traps. The real part of YDD exhibits strong frequency dispersion and an important degradation of the output resistance at high frequency. The imaginary part is characterized by a peak at a frequency decreasing with trap energy deeper in the gap, in agreement with experimental data on similar structures. Distributed local sources show that YDD is most sensitive to trap energy variations localized in the buffer region under the gate, peaking under the unsaturated portion of channel towards the source. Trap variations affect the output admittance when localized in depth into the buffer up to a 100 nm distance from the channel.

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Section: Sensitivity Of Real (Y Dd )mentioning

confidence: 99%

Section: Sensitivity Of Real (Y Dd )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TCAD Modeling of GaN HEMT Output Admittance Dispersion through Trap Rate Equation Green’s Functions

2023

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“…The model employs a regression-based method to detect health state transition points and applies an exponential random coefficient model with a Bayesian updating process to estimate time-to-failure distributions. A novel approach that combines technology computer-aided design (TCAD) simulation and machine learning (ML) techniques, is demonstrated to assist the analysis of the performance degradation of GaN HEMTs under hot-electron stress [3].…”

Section: Introductionmentioning

confidence: 99%

Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence

et al. 2023

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Interdependence is an inherent feature of the cyber-physical system. Small damage to one component in the system may affect several other components, leading to a series of failures, thus collapsing the entire system. Therefore, the system failure is often caused by the failure of one or more components. In order to solve this problem, this paper focuses on a failure propagation probability prediction method for complex electromechanical systems, considering component states and dependencies between components. Firstly, the key component set in the system is determined based on the reliability measure. Considering the three coupling mechanisms of mechanical, electrical, and information, a topology network model of the system is constructed. Secondly, based on the topology network model and fault data, the calculation method of influence degree between components is proposed. Three state parameters are used to express the risk point state of each component in the system through mathematical representation, and the correlation coefficient between the risk point state parameters is calculated and measured based on the uncertainty evaluation. Then, the influence matrix between the system risk points is constructed, and the fault sequence is predicted by using the prediction function of an Artificial Neural Network (ANN) to obtain the fault propagation probability. Finally, the method is applied to the rail train braking system, which verifies that the proposed method is feasible and effective.

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Optimization Conditions for High-Power AlGaN/InGaN/GaN/AlGaN High-Electron-Mobility Transistor Grown on SiC Substrate

Kim,

Park

2024

Materials

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In this study, we aimed to propose an optimal structure for an AlGaN/InGaN/GaN/AlGaN/SiC HEMT by investigating how the breakdown voltage varies with the thickness and composition of the InGaN layer. The breakdown voltage was shown to be highly dependent on the In composition. Specifically, as the In composition increased, the breakdown voltage rapidly increased, but it exhibited saturation when the In composition exceeded 0.06. Therefore, it is desirable to maintain the In composition at or above 0.06. The variation in breakdown voltage due to thickness was relatively small compared to the variation caused by In composition. While the breakdown voltage remained nearly constant with increasing thickness, it began to decrease when the thickness exceeded 10 nm. Hence, the thickness should be kept below 10 nm. Additionally, as the In composition increased, the subthreshold swing (SS) also increased, but the drain current value was shown to increase. On the other hand, it was observed that the SS value in the transfer characteristics and the current–voltage characteristics were almost unaffected by the thickness of the InGaN layer.

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Degradation Prediction of GaN HEMTs under Hot-Electron Stress Based on ML-TCAD Approach

Cited by 3 publications

References 42 publications

TCAD Modeling of GaN HEMT Output Admittance Dispersion through Trap Rate Equation Green’s Functions

TCAD Modeling of GaN HEMT Output Admittance Dispersion through Trap Rate Equation Green’s Functions

Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence

Optimization Conditions for High-Power AlGaN/InGaN/GaN/AlGaN High-Electron-Mobility Transistor Grown on SiC Substrate

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