Data‐Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning

Abstract

Tunnel oxide passivated contacts (TOPCon) have recently 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, we have compiled a dataset of all device data found in the 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 we observe 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, we were able to reinforces some commonly held hypotheses related to the performance differences between different device architectures. We also identify 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, which would further increase the value of statistical analysis like this as well as enable a larger part of the ML toolbox to be used.This article is protected by copyright. All rights reserved