Thin Film Solar Cells with n-type CdTe Absorber and p-type ZnTe Window Layers

Palekis, Vasilios; Wang, Wei; Elahi, Sheikh Tawsif; Alom, Zahangir; Ferekides, C.S.

doi:10.1109/pvsc43889.2021.9518840

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…5, 102,163–171 and a leading material for a wide range of potential applications in solid-state electronic devices such as solar cells, green yellow light-emitting diodes, fluorescent screens, interference filters, laser diodes, solar panel displays, integrated circuits, terahertz radiation detectors, switching devices, gamma-ray detectors, nano-sensors, gas sensors, biomedical imaging devices, electroluminescent devices, electromagnetic field coatings, protective coatings, photodetectors, tandem solar cells, and quantum well structures. 103,172–182…”

Section: Impact Of Doping and Post Treatment On Physical Propertiesmentioning

confidence: 99%

An overview on the role of ZnTe as an efficient interface in CdTe thin film solar cells: a review

et al. 2022

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Section: Impact Of Doping and Post Treatment On Physical Propertiesmentioning

confidence: 99%

An overview on the role of ZnTe as an efficient interface in CdTe thin film solar cells: a review

et al. 2022

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“…Additionally, we have also identified experimental evidence supporting the efficacy of fluorine doping in CdTe, which aligns with our results and suggests fluorine as a promising dopant, increasing carrier density to around 10 17 cm −3 . 32 Furthermore, as research groups show growing interest in ntype CdTe solar cells, 33 we have also identified potential dopants in Table SIII. Group VII elements are predicted to be effective for rendering CdTe n-type, potentially achieving carrier densities exceeding 10 15 cm −3 .…”

Section: ■ Methodsmentioning

confidence: 99%

Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals

Xiang,

Soh,

Dunham

2024

J. Phys. Chem. C

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In this work, we present an exploration of deep learning models for predicting defect properties in cubic phase semiconductors. The nature of impurity energy levels strongly influences the performance of semiconductors in a wide range of applications, such as solar cells, field effect transistors, and qubits for quantum computing. In this work, we employ two types of deep learning models, a crystal defect graph neural network and a chemical environment-encoded artificial neural network, to predict defect properties. The models are trained on a data set of charge-dependent defect formation energies obtained from density functional theory computations and descriptors based on elemental properties, defect local environment, and relevant semiconductor properties. We assess the models' performance and showcase their capability in optimizing semiconductor devices, particularly when used in tandem with compositionally constrained thermodynamics and technology computer-aided design models.

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