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

Dragica Vasileska

doi:10.1117/12.3005751

8 March 2024 Drift-diffusion-reaction and machine learning modeling of Cu diffusion in CdTe solar cells

Dragica Vasileska

Proceedings Volume 12881, Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII; 1288106 (2024) https://doi.org/10.1117/12.3005751
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

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 thin-film 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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dragica Vasileska "Drift-diffusion-reaction and machine learning modeling of Cu diffusion in CdTe solar cells", Proc. SPIE 12881, Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII, 1288106 (8 March 2024); https://doi.org/10.1117/12.3005751

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