Kinetic Simulations of Cu Doping in Chlorinated CdSeTe PV Absorbers

Sankin, Igor; Krasikov, Dmitry

doi:10.1002/pssa.201800887

Cited by 21 publications

(18 citation statements)

References 20 publications

(40 reference statements)

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“…We train separate ML models for impurity properties computed with PBE and HSE, and explore how models trained for lower fidelity (presumably, PBE) can inform the higher fidelity (presumably, HSE) predictions. We simulate impurities in several different defect sites in any CdX compound: one cation site (M Cd , where M is the impurity atom), one or two anion sites (M X ) and three or four interstitial sites (M i ), based on whether it is a pure or mixed anion composition 47 ; each of these sites have been pictured for CdTe in Fig An outline of the work presented in this manuscript is shown in in Fig. 1(d).…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…[16,21,25,60] Contrasting bulk recombination properties are especially clear in Al 2 O 3 passivated heterostructures (without a bilayer absorber and without the pn junction space charge field) where lifetimes are more than ten times higher for CdSeTe (430 ns) than for CdTe (27 ns). [55] In summary, we suggest that in addition to SRH recombination center point defects such as Te Cd , [57,58] Cu Cd , [42] and others, [46,56,61,62] CdSe/CdTe defect models also need to consider the origin and impact of charges at extended defects, including GBs.…”

Section: Contrasting Cdsete and Cdte Electro-optical Propertiesmentioning

confidence: 96%

Identification of Recombination Losses in CdSe/CdTe Solar Cells from Spectroscopic and Microscopic Time‐Resolved Photoluminescence

2021

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Due to the lowest‐cost and best reliability, CdTe solar cells are the leading thin‐film photovoltaic technology. Increasing open‐circuit voltage by reducing recombination represents the most promising path toward further improvements. Analysis is needed to identify limitations that cause efficiency losses. To achieve this goal for Cu‐doped CdSe/CdTe solar cells, time‐resolved spectroscopy and microscopy are developed and applied. Recombination lifetimes and radiative efficiency identify that defect‐mediated recombination is the dominant voltage loss mechanism. When carrier lifetimes are averaged over many crystalline grains, they increase from 180 to 430 ns when Al2O3 is applied to the back contact. The quasi‐Fermi‐level splitting correspondingly increases from 880–905 to 906–931 mV, indicating a pathway to overcome the long‐standing 900 mV voltage limitation. However, the dominant recombination losses are attributed to the absorber bulk. From microscopic carrier lifetime measurements, it is identified that space charge fields due to charged grain boundaries (GBs) lead to recombination in the CdTe absorber bulk. At high injection, GB space charge fields are screened, but that occurs above 1 Sun excitation conditions. Alloying with selenium in the near‐interface CdSeTe absorber region reduces GB losses and is identified as one of the factors leading to high radiative and power conversion efficiency.

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“…However, CdTe solar cell based on the sputtered CdSe process shows much lower V oc and FF due to the Se-related defects. [8] These defects can be found both in the CdSe x Te 1Àx layer [9][10][11][12] and at the SnO 2 :F (FTO)/CdSe front interface. [13,14] Although many attempts have been made to improve the front interface quality, [15,16] so far no efforts solve the dilemma and all the cells show efficiency less than 15%.…”

Section: Introductionmentioning

confidence: 99%

Improving CdTe‐Based Thin‐Film Solar Cell Efficiency with the Oxygenated CdSe Layer Prepared by Sputtering Process

Zhou²,

Qin³

et al. 2020

Physica Status Solidi (a)

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Sputtered CdSe window layer can improve the short-circuit current density (J sc) of CdTe-based thin-film solar cell, however, meanwhile degrade the open-circuit voltage (V oc) and fill factor (FF). Herein, an oxygenation process is introduced during CdSe sputtering to improve this window layer's property for better device performance. The influence of oxygen on the CdSe is similar as the effect of oxygen on CdS. Incorporation of oxygen into the CdSe film promotes the formation of CdSeO 3 phase. Both V oc and FF of the CdTe solar cells are improved due to the formation of CdSeO 3 , and high efficiency (Eff.) of 18.6% is achieved. Efficiency improvement at module level is also demonstrated after applying the CdSe:O process in production line. This progress contributes to the CdTe-based thin-film module production for further efficiency improvement and cost reduction.

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Kinetic Simulations of Cu Doping in Chlorinated CdSeTe PV Absorbers

Cited by 21 publications

References 20 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

Identification of Recombination Losses in CdSe/CdTe Solar Cells from Spectroscopic and Microscopic Time‐Resolved Photoluminescence

Improving CdTe‐Based Thin‐Film Solar Cell Efficiency with the Oxygenated CdSe Layer Prepared by Sputtering Process

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