Key Takeaways & Executive Findings
- •• Machine learning models (Lasso, random forest, multilayer perceptron) were applied to analyze interconnecting layer parameters in perovskite/silicon tandem solar cells. • Key experimental parameters influencing interconnecting layer performance were identified through feature importance analysis. • The optimized interconnecting layer achieved a proof-of-concept efficiency of 38.17%. • The study provides a data-driven approach to accelerate the development of high-efficiency tandem solar cells.
Abstract
As the development of single-junction solar cells reaches a bottleneck, tandem solar cells have emerged as a critical pathway to further enhance power conversion efficiency. Among them, monolithic perovskite/silicon heterojunction tandem solar cells are currently the fastest-growing technology, achieving the highest efficiencies at relatively low costs. The interconnecting layer, which connects the two sub-cells, plays a crucial role in tandem cell performance. It collects electrons and holes from the respective sub-cells and facilitates recombination and tunneling at the interface. Therefore, the properties of the interconnecting layer are pivotal to the overall device performance. In this work, we applied statistical analysis and machine learning algorithms to systematically analyze the interconnecting layer. A comprehensive dataset on interconnecting layer parameters was established, and predictive modeling was performed using Lasso linear regression, random forest, and multilayer perceptron (a type of neural network). The analysis revealed key feature importance for experimental parameters, providing valuable insights into the application of interconnecting layers in perovskite/silicon heterojunction tandem solar cells. The final optimized interconnecting layer can achieve a proof-of-concept efficiency of 38.17%, providing guidance and direction for the development of monolithic perovskite/silicon tandem solar cells.
1. Introduction
Single-junction solar cells have approached their theoretical efficiency limits, prompting the exploration of tandem architectures to further enhance power conversion efficiency. Among various tandem configurations, monolithic perovskite/silicon heterojunction tandem solar cells have emerged as a leading technology due to their potential for high efficiency at relatively low manufacturing costs. These devices consist of a perovskite top cell and a silicon bottom cell connected by an interconnecting layer, which is critical for charge collection and recombination. The interconnecting layer must efficiently collect electrons and holes from the respective sub-cells and facilitate their recombination and tunneling at the interface, making its properties pivotal to overall device performance.
Despite the importance of the interconnecting layer, its optimization has traditionally relied on empirical trial-and-error methods, which are time-consuming and resource-intensive. In this work, we employ statistical analysis and machine learning algorithms to systematically analyze the interconnecting layer. By constructing a comprehensive dataset of interconnecting layer parameters and applying predictive models, we aim to identify key factors that influence performance and accelerate the development of optimized interconnecting layers. Our findings not only provide insights into the design of interconnecting layers but also demonstrate the potential of machine learning in advancing photovoltaic research.
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Xuejiao Wang, Guanlan Chen, Ying Liu, Guangyi Wang, Wei Han, Jin Wang, Pengfei Liu, Jilei Wang, Shaojuan Bao, Bo Yu, Ying Liu, Xinliang Chen, Shengzhi Xu, Ying Zhao, Xiaodan Zhang (2025). Machine learning facilitates the development of interconnecting layers for perovskite/silicon heterojunction tandem solar cells with proof-of-concept efficiency > 38%. SinoTechIntel Verified Research. https://doi.org/10.1088/1674-4926/25050011
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Frequently Asked Questions
What is the significance of the interconnecting layer in tandem solar cells?
The interconnecting layer is crucial in monolithic perovskite/silicon tandem solar cells as it connects the top and bottom sub-cells, collecting electrons and holes and facilitating recombination and tunneling at the interface. Its properties directly impact the overall device performance and efficiency.
How was machine learning applied in this study?
The study used statistical analysis and machine learning algorithms, including Lasso linear regression, random forest, and multilayer perceptron, to analyze a comprehensive dataset of interconnecting layer parameters. These models identified key experimental parameters and predicted optimal conditions for high efficiency.
What efficiency was achieved with the optimized interconnecting layer?
The optimized interconnecting layer achieved a proof-of-concept efficiency of 38.17%, demonstrating the effectiveness of the machine learning approach in guiding the development of high-performance tandem solar cells.
What are the main advantages of using machine learning in solar cell development?
Machine learning enables systematic analysis of complex parameter spaces, reduces the need for exhaustive experimental trials, and provides insights into feature importance, thereby accelerating the optimization process and reducing development costs.
What are the key parameters identified for interconnecting layer performance?
The study identified key experimental parameters through feature importance analysis, though specific parameters are detailed in the full paper. These parameters are critical for optimizing the interconnecting layer to achieve high efficiency.
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