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Data-Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning
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DOI：10.1002/adma.202309351.png)
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Abstract
En 中文
Tunnel oxide passivated contacts (TOPCon) have gained interest as a way to increase the energy conversion efficiency of silicon solar cells, and the International Technology Roadmap of Photovoltaics forecasts TOPCon to become an important technology despite a few remaining challenges. To review the recent development of TOPCon cells, this work has compiled a dataset of all device data found in current literature, which sums up to 405 devices from 131 papers. This may seem like a surprisingly small number of cells given the recent interest in the TOPCon architecture, but it illustrates a problem of data dissemination in the field. Notwithstanding the limited number of cells, there is a great diversity in cell manufacturing procedures, and this work observes a gradual increase in performance indicating that the field has not yet converged on a set of best practices. By analyzing the data using statistical methods and machine learning (ML) algorithms, this work is able to reinforces some commonly held hypotheses related to the performance differences between different device architectures. This work also identifies a few more unintuitive feature combinations that would be of interest for further experimentally studies. This work also aims to inspire improvements in data management and dissemination within the TOPCon community. This study provides two features that may enhance experimental studies and evaluations on TOPCon solar cells technology: 1) the compilation of a dataset built from current literature, and: 2) a series of rules on cell performance and heuristics for optimization that are obtained based on this dataset, which could serve as a source of inspiration for further experimental studies.image
Keywords:
accelerated design
machine learning
solar cells
TOPCon