ChemEngineering (Sep 2023)

Numerical Simulation, Preparation, and Evaluation of Cu(In, Ga)Se<sub>2</sub> (CIGS) Thin-Film Solar Cells

  • Borhan Albiss,
  • Mohammad Al-Widyan

DOI
https://doi.org/10.3390/chemengineering7050087
Journal volume & issue
Vol. 7, no. 5
p. 87

Abstract

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This study presents the numerical simulation, optimization, preparation, and characterization of Cu(In, Ga)Se2 (CIGS) thin-film solar cells (TFSCs). Different cell parameters were investigated, including Ga/(Ga+In) (GGI) ratios, the thicknesses of CIGS absorption layers, the fill factor (FF), the open-circuit voltage (Voc), and the short-circuit current (Isc). The effects of the simulated parameters on the power conversion efficiency (η) of each prototype CIGS cells were investigated. The optimal GGI ratio was approximately 0.6. Using COMSOL Multiphysics software, a CIGS layer thickness of 2 μm and an η of 17% was calculated, assuming constant operating temperatures. Moreover, prototype CIGS solar cells with various compositions were prepared via a simple and cost-effective method based on sol–gel, sonication, and spin-coating techniques. The microstructures and electrical and optical properties of the CIGS-based solar cells were evaluated using current–voltage (I-V) characteristics, scanning electron microscopy (SEM), X-ray diffraction, atomic force microscopy (AFM), and UV-vis spectroscopy. The elemental compositions of the solar cell layers were evaluated via energy-dispersive X-ray fluorescence (EDXRF). The obtained results were compared with the experimental results. For example, in a prototype cell with a CIGS absorption layer thickness of 2 μm and a GGI ratio of 0.6, the experimental value of η was about 15%. Our results revealed that the agreement between the simulation results and the experimental findings for most of the simulated parameters is quite good. These findings indicate that a non-destructive analysis based on EDXRF is a versatile tool for evaluating CIGS solar cells in a very short time with excellent repeatability for both layer composition and thickness.

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