Predicted Theoretical Efficiency for New Intermediate Band Solar Cells (IBSC) Based on GaAs1-xNx

Boumesjed, A.; Mazari, H.; Ameur, K.

doi:10.12816/0048938

“…For instance, III-V semiconductors such as GaN, GaP, GaAlP, AlP, BP etc. are interesting materials for photodiodes, solar cells, and in recent times, have been studied for intermediate band photovoltaic applications [64][65][66][67] . A quick screening of impurity atoms that can not only change the equilibrium Fermi level, but also create energy level(s) in the band gap, can be made possible using machine learned models to predict impurity properties.…”

Section: Discussionmentioning

confidence: 99%

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides

Mannodi-Kanakkithodi

¹

,

Toriyama

²

,

Şen

³

et al. 2020

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The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity, and thus the semiconductor's performance in solar cells, photodiodes, and optoelectronics. The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate. In this work, we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe, CdSe, and CdS can lead to accurate and generalizable predictive models of defect properties. By converting any semiconductor + impurity system into a set of numerical descriptors, regression models are developed for the impurity formation enthalpy and charge transition levels. These regression models can subsequently predict impurity properties in mixed anion CdX compounds (where X is a combination of Te, Se and S) fairly accurately, proving that although trained only on the end points, they are applicable to intermediate compositions. We make machine-learned predictions of the Fermi-level-dependent formation energies of hundreds of possible impurities in 5 chalcogenide compounds, and we suggest a list of impurities which can shift the equilibrium Fermi level in the semiconductor as determined by the dominant intrinsic defects. These 'dominating' impurities as predicted by machine learning compare well with DFT predictions, revealing the power of machine-learned models in the quick screening of impurities likely to affect the optoelectronic behavior of semiconductors.

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“…The theoretical approaches in the previous study show that the efficiency enhances from 15.0% to the highest 38.20% till now. From the simulation works, we have determined larger V oc and ŋ compared to the reported values [13,14,22,28]. The value of FF and J sc obtained from the numerical study is also higher than that in most of the previous reported values.…”

Section: Enhancement Of Performance Parameter P-gaasn Solar Cellmentioning

confidence: 49%

“…This research demonstrates the modeled p-GaAsN solar cell with AlGaAs blocking layer can achieve the improvement of V oc and . 1.42 [14,18] 1.33 [14] 1.81 [19,20] Electron affinity, χ (eV) 4.07 [14,18,21] 4.071 [14,22] 3.74 [19,20] Dielectric permittivity (relative), ɛ r 12.5 [14,23] 12.38 [14] 12.1 [20,24] Effective conduction band density, N C (cm -3 ) 4.33×10 17 [14,22,25] 4.66×10 17 [14][22]…”

Section: Introductionmentioning

confidence: 65%

“…Mobility of electron, µ n (cm 2 V -1 s -1 ) 8500 [14,18,21] 6538.46 [14,22] 2300 [19,20] Mobility of hole, µ p (cm 2 V -1 s -1 ) 400 [14,18,21] 397.72 [14,22] 150 [19][19] * Concentration of donor, N D (cm -3 ) 1.0×10 16 1.0×10 14 1.0×10 14 1.0×10 1.0×10 -17 -1.0×10 -17 Capture cross section of hole for acceptor defect (cm -2 )…”

Section: Device Structure and Simulationmentioning

confidence: 99%

“…In order to investigate the impact of doping density, the acceptor density N A in p + -GaAs and p ++ -GaAs layers is varied from 5.0×10 14 to the enhancement of the reverse saturation current and electrical conductivity [15,34]. At the value of large N A , recombination of electron-hole pair reduces the number of carrier at the electrode which results in constant J sc [30,34].…”

Section: Influence Of Doping Concentration On Pv Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Design and Simulation of GaAsN Based Solar Cell with AlGaAs Blocking Layer for Harvesting Visible To Near-Infrared Light

Haque

¹

,

Ali

²

,

Halim

³

et al. 2021

Preprint

0

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In the present study, the performance parameters of GaAsN dilute nitride-based semiconductor solar cell with and without AlGaAs blocking layers have been investigated in detail by Solar Cell Capacitance Simulator in one dimensional software program (SCAPS-1D). The thickness of absorber, buffer, and blocking layers are varied to achieve the improvement of open circuit voltage, short circuit current, fill factor, efficiency and also to optimize the device structure. The impact of doping and defect densities on the solar cell performance parameters have been analyzed minutely inside the absorber, buffer, and blocking layers. The solar cell thermal stability parameters are also investigated in the temperature region from 273K to 373K. The efficiency of 43.90% and 40.05% are obtained from the proposed solar cells with and without AlGaAs blocking layer, respectively. The present findings may provide insightful approach for fabricating feasible, cost effective, and efficient dilute nitride solar cell.

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Design and analysis of GaAsN based solar cell for harvesting visible to near-infrared light

Haque¹,

Ali²,

Hossain³

et al. 2022

Phys. Scr.

3

0

View full text Add to dashboard Cite

In the present study, the performance parameters of GaAsN dilute nitride-based semiconductor solar cell with and without AlGaAs blocking layers have been investigated in detail by Solar Cell Capacitance Simulator in one dimensional software program (SCAPS-1D). The thickness of absorber, buffer, and blocking layers are varied to achieve the improvement of open circuit voltage, short circuit current, fill factor, efficiency and also to optimize the device structure. The impact of doping and defect densities on the solar cell performance parameters have been analyzed minutely inside the absorber, buffer, and blocking layers. The solar cell thermal stability parameters are also investigated in the temperature region from 273K to 373K. The efficiency of 43.90% and 40.05% are obtained from the proposed solar cells with and without AlGaAs blocking layer, respectively. The present findings may provide insightful approach for fabricating feasible, cost effective, and efficient dilute nitride solar cell.

show abstract

Predicted Theoretical Efficiency for New Intermediate Band Solar Cells (IBSC) Based on GaAs1-xNx

Cited by 4 publications

References 0 publications

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides

Design and Simulation of GaAsN Based Solar Cell with AlGaAs Blocking Layer for Harvesting Visible To Near-Infrared Light

Design and analysis of GaAsN based solar cell for harvesting visible to near-infrared light

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