Enhanced Efficiency of Intermediate Band Solar Cells Through MLP-PSpice Modeling

Intermediate Band Solar Cells (IBSCs) represent a novel and promising advancement in solar cell technology. These cells hold great potential for high conversion efficiencies, with theoretical values reaching 63.2% under black body illumination and 65.1% under the AM1.5 spectrum. Notably, these efficiency levels are comparable to those achieved by optimized triple-junction solar cells, which attain 63.8% and 67.0%, respectively. This paper focuses on the development of an IBSC structure based on ZnTeO and leverages the power of an Artificial Neural Network (ANN) to model and enhance the performance of the IBSC. The use of an ANN brings significant advantages, particularly in reducing the need for an extensive database. By employing a small representative dataset, the ANN-based approach proves to be highly effective in optimizing the IBSC's efficiency. Importantly, the proposed methodology is not limited to IBSCs alone; it can be extended to other types of solar cells as well. This versatility promotes the realization of promising photovoltaic devices that boast both low-cost manufacturing and high efficiency. This research marks a significant step forward in the quest for efficient and affordable solar energy conversion technologies.