A Unified 2D Solver for Modeling Carrier and Defect Transport in Photovoltaic Devices

Shaik, Abdul R.; Brinkman, Daniel; Sankin, Igor; Krasikov, Dmitry; Ringhofer, Christian; Vasileska, Dragica

doi:10.1109/pvsc.2018.8547494

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Within the PVRD-FASP framework, the design of a solar cell of interest begins with the definition of a new project from scratch using new database and new dictionaries (See Ref. [30]). The user can also open an existing database that includes dictionaries and projects that have been designed before.…”

Section: Implementation Of ML Algorithm and Simulation Resultsmentioning

confidence: 99%

Drift-diffusion-reaction and machine learning modeling of Cu diffusion in CdTe solar cells

Vasileska

2024

Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII

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In this paper we introduce the PVRD-FASP solver for studying carrier and defect transport in CdTe solar cells on an equal footing by solving 1D and 2D drift-diffusion-reaction model equations. The diffusion constants and activation energies of the defect and the defect chemical reactions require reaction rate constants that are calculated using density functional theory (DFT). The PVRD-FASP solver can propose solutions that can reduce the development cost of thinfilm photovoltaics (TFPV) because up-and down-stream process optimization, required due to complex interactions, is replaced by predictive modeling. An in-house implementation of a machine-learning approach for modeling of Cu diffusion in the CdTe absorber layer of the CdTe solar cell is also discussed.

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Section: Implementation Of ML Algorithm and Simulation Resultsmentioning

confidence: 99%

Drift-diffusion-reaction and machine learning modeling of Cu diffusion in CdTe solar cells

Vasileska

2024

Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII

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“…In the case of IBSC, there are electrons in IB. The electrostatic potential can be given by the Poisson equation [28][29][30].…”

Section: Methods For Simulating Graphene/si Qdibscmentioning

confidence: 99%

Design and simulation of type-I graphene/Si quantum dot superlattice for intermediate-band solar cell applications

Sarkhoush

Saghai

Soofi

2022

Front. Optoelectron.

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Recent experiments suggest graphene-based materials as candidates for use in future electronic and optoelectronic devices. In this study, we propose a new multilayer quantum dot (QD) superlattice (SL) structure with graphene as the core and silicon (Si) as the shell of QD. The Slater–Koster tight-binding method based on Bloch theory is exploited to investigate the band structure and energy states of the graphene/Si QD. Results reveal that the graphene/Si QD is a type-I QD and the ground state is 0.6 eV above the valance band. The results also suggest that the graphene/Si QD can be potentially used to create a sub-bandgap in all Si-based intermediate-band solar cells (IBSC). The energy level hybridization in a SL of graphene/Si QDs is investigated and it is observed that the mini-band formation is under the influence of inter-dot spacing among QDs. To evaluate the impact of the graphene/Si QD SL on the performance of Si-based solar cells, we design an IBSC based on the graphene/Si QD (QDIBSC) and calculate its short-circuit current density (Jsc) and carrier generation rate (G) using the 2D finite difference time domain (FDTD) method. In comparison with the standard Si-based solar cell which records Jsc = 16.9067 mA/cm2 and G = 1.48943 × 1028 m−3⋅s−1, the graphene/Si QD IBSC with 2 layers of QDs presents Jsc = 36.4193 mA/cm2 and G = 7.94192 × 1028 m−3⋅s−1, offering considerable improvement. Finally, the effects of the number of QD layers (L) and the height of QD (H) on the performance of the graphene/Si QD IBSC are discussed. Graphical abstract

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A Unified 2D Solver for Modeling Carrier and Defect Transport in Photovoltaic Devices

Cited by 2 publications

References 11 publications

Drift-diffusion-reaction and machine learning modeling of Cu diffusion in CdTe solar cells

Drift-diffusion-reaction and machine learning modeling of Cu diffusion in CdTe solar cells

Design and simulation of type-I graphene/Si quantum dot superlattice for intermediate-band solar cell applications

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