DOI: 10.1155/mse/1029029 ISSN: 1687-5591

Performance Analysis of Regular (n–i–p) and Inverted (p–i–n) Perovskite Solar Cells Using SCAPS‐1D: A Comparative Study Based on Fabrication Parameters

Bonsa Regassa Hunde, Alemu Merga Eticha, Sachin Kalsi

Although perovskite solar cells hold significant promise for next‐generation photovoltaic technologies, their development is hindered by challenges such as nonradiative recombination losses, limited scalability, and long‐term stability. To better understand the influence of device architecture and key fabrication parameters on photovoltaic performance, this study models and analyzes two widely adopted PSC structures—the inverted planar p–i–n and conventional n–i–p architectures—using the Solar Cell Capacitance Simulation One‐Dimensional (SCAPS‐1D) software. The effects of electron transport layer (ETL), hole transport layer (HTL), and perovskite layer thicknesses, as well as perovskite layer defect density, on the key photovoltaic parameters were systematically investigated. The results reveal that increasing the perovskite layer thickness and reducing defect density significantly improve the power conversion efficiency (PCE) of both structures. The p–i–n architecture achieved a higher PCE of 22.52% because of its improved fill factor (FF), whereas the n–i–p structure exhibited higher open‐circuit voltage (Voc) and short‐circuit current density (Jsc), resulting in a PCE of 21.07%. Reducing the defect density from 1E+16 (cm −3 ) to 1E+12 (cm −3 ) and increasing perovskite layer thickness from 500 nm to 800 nm increased the maximum PCE to 26.88% for the regular n–i–p PSC and 28.22% for the inverted p–i–n PSC. Overall, based on the theoretical analysis and comparison with experimental results reported by other researchers, the inverted p–i–n architecture emerges as a promising candidate because of its superior efficiency, lower hysteresis, and fabrication simplicity, although the selection of an effective HTL remains a significant challenge. These findings provide valuable insights for the design and optimization of efficient PSCs.

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