Photoreflectance studies of optical transitions in GaNPAs intermediate band solar cell absorbers

Żelazna, K.; Kudrawiec, R.; Luce, Alexander; Yu, K. M.; Kuang, Y. J.; Tu, C. W.; Walukiewicz, W.

doi:10.1016/j.solmat.2018.08.024

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…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

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…Therefore, to compute ω p we need a suitable expression for . The well-known random phase approximation relation, which connects density susceptibility of a system χ to , is particularly suitable for a case where the main dynamics are due to mobile electrons [24] (q, ω) = 1 − 4πe 2 q 2 χ(q, ω), (10) where e is the electron charge and q and ω are the external wavevector and frequency, respectively. Since we seek (0, ω p ) = 0, this form reduces the problem to finding the long-wavelength limit of χ.…”

Section: B Long-wavelength Limit Of the Susceptibilitymentioning

confidence: 99%

“…The dramatic band gap drop, first observed in the 1990s [2], soon found applications such as developing quantum well laser diodes [3] and high efficiency multijunction solar cells [4]. Later, building on the BAC model of two split bands in HMAs, they were used to implement intermediate band solar cells [5][6][7][8][9][10][11], which is another scheme for harvesting sub-band-gap photons [12]. A collection of recent developments in the study of this class of semiconductor alloys can be found in a Special Topic issue of Journal of Applied Physics [13].…”

Section: Introductionmentioning

confidence: 99%

Plasma frequency in doped highly mismatched alloys

Allami,

Krich

2020

Preprint

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Highly mismatched alloys (HMAs) have band structures strongly modified due to the introduction of the alloying element. We consider HMAs where the isolated state of the alloying element is near the host conduction band, which causes the conduction band to split into two bands. We determine the bulk plasma frequency when the lower-energy band is partially occupied, as by doping, using a semi-analytical method based on a disorder-averaged Green's function. We include the nontrivial effects of interband transitions to the higher-energy band, which limit the plasma frequency to be less than an effective band gap. We show that the distribution of states in the split bands causes plasmons in HMAs to behave differently than plasmons in standard metals and semiconductors. The effective mass of the lower split band m * changes with alloy fraction, and we find that the plasmon frequency with small carrier concentration n scales with √ n/m * rather than the n/m * that is expected in standard materials. We suggest experiments to observe these phenomena. Considering the typical range of material parameters in this group of alloys and taking a realistic example, we suggest that HMAs can serve as highly tunable low-frequency plasmonic materials.

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“…In such HMA's with a CB anticrossing, the splitting can generate a narrow intermediate band, E − , in the original bandgap of the host. The presence of the narrow band makes HMA's a candidate for implementing intermediate band solar cells [5][6][7][8][9][10][11], which have the potential to break the Shockley-Queisser limit on solar cell efficiency [12]. Going beyond the BAC, the HMA electronic structure has unusual properties distinct from standard semiconductors [13], which can manifest in their plasmonic properties when doped [14].…”

Section: Introductionmentioning

confidence: 99%

Absorption spectrum of doped highly mismatched alloys

Allami¹,

Krich²

2022

Preprint

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Highly mismatched alloys (HMA's) are a class of semiconductor alloys with large electronegativity differences between the alloying elements. We predict the absorption spectrum due to transitions between the split bands of a doped highly mismatched alloy with a conduction band anticrossing. We analyze the joint densities of states for both direct and indirect transitions between the split bands. The resulting spectrum has features that reveal the unusual state distribution that is characteristic of HMAs, hence providing valuable insight into their electronic structure. In particular, we predict a peak near the absorption edge, which arises due to the suppression of direct transitions at large momenta. We present analytic forms for the near-absorption-edge and large-energy spectra, showing that they are qualitatively different from those in standard parabolic semiconductors. In particular, as a result of suppressed direct transitions, indirect transitions dominate the spectrum away from the edge of absorption. A. HMA's special distribution of states and the absorptivityAccording to the BAC model [4], large differences in the electronegativities of the mismatched elements compared to the host leads to the formation of localized states

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Photoreflectance studies of optical transitions in GaNPAs intermediate band solar cell absorbers

Cited by 9 publications

References 29 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

Plasma frequency in doped highly mismatched alloys

Absorption spectrum of doped highly mismatched alloys

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