Reverse bias leakage current mechanism of AlGaN/InGaN/GaN heterostructure

Chakraborty, Apurba; Ghosh, Saptarsi; Mukhopadhyay, Partha; Jana, Sanjay Kumar; Dinara, Syed Mukulika; Bag, Ankush; Mahata, Mihir Kumar; Kumar, Rahul; Das, Subhashis; Das, Poulami; Biswas, Dhrubes

doi:10.1007/s13391-015-5249-9

Cited by 8 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Africa is the second-most populous and second-largest continent in the world. It covers 6% of the earth's total surface (20% of total land area) with a total area of about 30.3 × 10 6 km 2 [37]. Solar energy distribution in Africa is fairly uniform and the global solar horizontal irradiance for a larger proportion (85%) of the landscape is over 2000 kWh/m 2 /year.…”

Section: Area Of Studymentioning

confidence: 99%

Development and Comparison of Two Novel Hybrid Neural Network Models for Hourly Solar Radiation Prediction

et al. 2022

View full text Add to dashboard Cite

There are a lot of developing countries with inadequate meteorological stations to measure solar radiation. This has been a major drawback for solar power applications in these countries as the performance of the solar-powered system cannot be accurately forecasted. In this study, two novel hybrid neural networks namely; convolutional neural network/artificial neural network (CNN-ANN) and convolutional neural network/long short-term memory/artificial neural network (CNN-LSTM-ANN), have been developed for hourly global solar radiation prediction. ANN models are also developed and the performance of the hybrid neural network models is compared with it. This study contributes to the search for more accurate solar radiation estimation methods. The hybrid neural network models are trained/tested with data from ten different countries across Africa. Results from this study indicate that the performance of all the hybrid models developed in this study is superior to what has been presented in existing literature with their r values ranging from 0.9662 to 0.9930. CNN-ANN model is the best for solar radiation forecasting in Southern, Central, and West Africa. CNN-LSTM-ANN is better for East Africa while both CNN-ANN and CNN-LSTM-ANN are suitable for North Africa. CNN-ANN application for solar radiation prediction in Chad had the overall best performance with an r-value, MAE, RMSE, and MAPE of 0.9930, 15.70 W/m2, 46.84 W/m2, and 4.98% respectively. The integration of CNN and LSTM algorithms with an ANN model enhanced long-term computational dependency and reduce error terms for the model.

show abstract

Section: Area Of Studymentioning

confidence: 99%

Development and Comparison of Two Novel Hybrid Neural Network Models for Hourly Solar Radiation Prediction

et al. 2022

View full text Add to dashboard Cite

show abstract

“…实验研究表明, 在Al x Ga 1-x N/GaN单异质结中间生长一层In y Ga 1-y N作为沟道层, 使原先的单异质结转变为双异质结 . 与原先的单异质结 Al x Ga 1-x N/GaN相比, 双异质结Al x Ga 1-x N/In y Ga 1-y N/GaN具有更强的载流子限制, 更高的迁移率和 2DEG浓度, 同时可以显著改善器件性能 [10] . 这些优良特征促使实验研究工作者开展这方面的研究, Chakraborty等 [11] 用分子束外延法在GaN/Si(111) 上生长 AlGaN/InGaN异质结构, 同时研究AlGaN/ InGaN/GaN异质结的反向偏置漏电流机制; Bag 等 [12] 通过实验结果推测InGaN的不熔性阻碍了高质量AlGaN/InGaN异质结的外延生长, 针对高速器件, AlGaN/InGaN/GaN的性能可能优于 AlGaN/GaN的性能 ; Simin等 [13]…”

unclassified

Effect of inserted InGaN layer on the two-dimensional electron gas in AlxGa1–xN/InyGa1–yN/GaN

Li-Na¹,

Yan-Wu²

2021

Acta Phys. Sin.

View full text Add to dashboard Cite

This paper studies the changes in electronic transport properties caused by InGaN as an AlGaN/GaN insertion layer, and considers the effects of the spontaneous polarization and piezoelectric polarization of AlGaN and InGaN barrier layers on the surface density of polarized charge, and the concentration of two-dimensional electron gas (2DEG) in AlxGa1–xN/InyGa1–yN/GaN double heterojunction high-electron-mobility transistor. The InGaN thickness and interface roughness scattering, random dipole scattering and polar optical phonons under different In molar compositions are analyzed. The calculation results show that the interface roughness scattering and random dipole scattering have an important influence on the electron transport properties of the double heterojunction AlxGa1–xN/InyGa1–yN/GaN, and the polar optical phonon scattering has the weakest influence; 2DEG concentration, the strength of interface roughness scattering, random dipole scattering and polar optical phonon scattering are determined by the thickness of the InGaN barrier layer and the molar composition of In. This paper takes 2DEG in the AlxGa1–xN/InyGa1–yN/GaN double heterojunction as the research object, considering the barrier layer of finite thickness, taking into account the spontaneous polarization effect and piezoelectric polarization effect of each layer, and giving AlxGa1–xN/GaN 2DEG characteristics in the InyGa1–yN/GaN double heterostructure, discussing the scattering of 2DEG concentration and interface roughness by changing the In molar composition and the thickness of the InGaN barrier layer under the same Al molar composition and the thickness of the AlGaN barrier layer, Random dipole scattering and polar optical phonon scattering. The results of the present study are of great significance in controlling the 2DEG concentration in the AlxGa1–xN/InyGa1–yN/GaN double heterojunction structure and improving the electron mobility. This paper presents the analytical expression of 2DEG concentration ns in AlxGa1–xN/InyGa1–yN/ GaN double heterostructure. The effects of the thickness of the InGaN insertion layer and the molar composition of indium on the 2DEG concentration, interface roughness scattering, random dipole scattering and total mobility are studied. According to the theoretical calculation results, on condition that the physical properties of the AlGaN barrier layer remain unchanged, choosing the appropriate InGaN barrier layer thickness and In molar composition concentration can better control the 2DEG concentration and carrier mobility. These results are beneficial to widely using the double heterojunction AlxGa1–xN/InyGa1–yN/GaN in actual nitride based semiconductor devices.

show abstract

“…These features have inspired experiments to compare the performance characteristics of electron devices based on an AlGaN= GaN heterostructure versus an AlGaN=InGaN=GaN heterostructure. [19][20][21][22] On the other hand, most experimental and theoretical studies pertaining to distributed surface donor states are focused on AlGaN=GaN-, AlGaN=AlN=GaN-, and InAlN=AlN=GaN-based heterostructures. 2,4,5,[23][24][25] In all these cases, the GaN buffer is considered as the channel material.…”

mentioning

confidence: 99%

“…Note that the previously reported simulations for AlGaN= InGaN=GaN (as well as AlGaN=InGaN) heterostructure(s) obtained the band diagram and carrier distribution by fitting simulations to the experimental measurements of the 2DEG densities and thus did not include surface states. 9,19,29,[40][41][42] Therefore, these methods cannot address the dependence of the SBH on the AlGaN material and In mole fraction of the channel. Instead, an iterative approach to determine the electron density due to the surface donor states must be taken.…”

mentioning

confidence: 99%

Modeling and simulation of AlGaN/InGaN/GaN double heterostructures using distributed surface donor states

Ghosh

Ganguly

2018

Jpn. J. Appl. Phys.

View full text Add to dashboard Cite

An analytical modeling framework is presented for the two-dimensional electron gas (2DEG) density and bare surface barrier height in AlGaN/InGaN/GaN double heterostructures, which use an InGaN layer as the conducting channel. The 2DEG formed at the AlGaN/InGaN heterointerface originates from the surface donor states on the AlGaN surface. The model is derived by the electrostatic analysis of different material interfaces. Numerical simulations are performed to determine the effect of a thick channel (InGaN layer). The presented results agree with the published experimental results, and they can easily be used for the advanced design and characterization of AlGaN/InGaN/GaN-based heterostructures.

show abstract

Reverse bias leakage current mechanism of AlGaN/InGaN/GaN heterostructure

Cited by 8 publications

References 24 publications

Development and Comparison of Two Novel Hybrid Neural Network Models for Hourly Solar Radiation Prediction

Development and Comparison of Two Novel Hybrid Neural Network Models for Hourly Solar Radiation Prediction

Effect of inserted InGaN layer on the two-dimensional electron gas in Al<sub><i>x</i></sub>Ga<sub>1–<i>x</i></sub>N/In<i><sub>y</sub></i>Ga<sub>1–<i>y</i></sub>N/GaN

Modeling and simulation of AlGaN/InGaN/GaN double heterostructures using distributed surface donor states

Contact Info

Product

Resources

About