Time-Efficient, Accurate, and Experimentally Grounded Optical Modeling of Multiscale-Textured Thin-Film Solar Cells

Journal Article (2025)
Author(s)

F.S. Saitta (TU Delft - Photovoltaic Materials and Devices)

G. Padmakumar (TU Delft - Photovoltaic Materials and Devices)

P. Perez Rodriguez (TU Delft - Photovoltaic Materials and Devices)

P.A. Procel Moya (TU Delft - Photovoltaic Materials and Devices)

R. Santbergen (TU Delft - Photovoltaic Materials and Devices)

Research Group
Photovoltaic Materials and Devices
DOI related publication
https://doi.org/10.1002/gch2.202500448
More Info
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Publication Year
2025
Language
English
Research Group
Photovoltaic Materials and Devices
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Abstract

Accurate prediction of optical performance in solar cells with multiscale-textured interfaces is essential for optimizing light management in next-generation photovoltaics. For the first time, a systematic validation of two complementary modeling approaches is carried out on experimentally fabricated thin-film silicon (TF Si) solar cells: rigorous coupled-wave analysis (RCWA), offering a full electromagnetic solution but constrained by boundary conditions, and a ray optics model, operating in the refractive regime. The study involves two device architectures: an a-Si:H single-junction cell on commercial Asahi VU-type glass with random nanotextures, and an nc-Si:H single-junction cell on novel micro-periodic honeycomb-textured glass developed in-house. Simulated and measured external quantum efficiency (EQE) and total front reflection losses (1-R) are benchmarked using the root mean squared error (RMSE). The ray model shows deviations of only 2%–6%, comparable to RCWA, while reducing computation time from 1 week to less than 30 min. Applied to an a-Si:H/nc-Si:H tandem device on honeycomb-textured glass, ray optics reproduced the optical response with spectral deviations below 6% and photocurrent mismatch under 0.2 mA/cm2. These findings uniquely establish ray optics, when combined with accurate optical constants and realistic interface morphologies, as a reliable and computationally efficient predictive tool broadly transferable to thin-film technologies, including perovskites.