Please use this identifier to cite or link to this item:
Title: Passivating contact-based tunnel junction Si solar cells using machine learning for tandem cell applications
Authors: Park, Hyunjung
Morisset, Audrey
Kim, Munho
Lee, Hae-Seok
Hessler-Wyser, Aïcha
Haug, Franz-Josef
Ballif, Christophe
Keywords: Engineering
Issue Date: 2023
Source: Park, H., Morisset, A., Kim, M., Lee, H., Hessler-Wyser, A., Haug, F. & Ballif, C. (2023). Passivating contact-based tunnel junction Si solar cells using machine learning for tandem cell applications. Energy and AI, 14, 100299-.
Project: T2EP50120-0001 
Journal: Energy and AI 
Abstract: Tandem solar cells are a key technology for exceeding the theoretical efficiency limit of single-junction cells. One of the most promising combinations is the silicon–perovskite tandem cells, considering their potential for high efficiency, fabrication on a large scale, and low cost. While most research focuses on improving each subcell, another key challenge lies in the tunnel junction that connects these subcells, significantly impacting the overall cell characteristics. Here, we demonstrate the first use of tunnel junctions using a stack of p+/n+ polysilicon passivating contacts deposited directly on the tunnel oxide to overcome the drawbacks of conventional metal oxide-based tunnel junctions, including low tunneling efficiency and sputter damage. Using Random Forest analysis, we achieved high implied open circuit voltages over 700 mV and low contact resistivities of 500 mΩ cm2, suggesting fill factor losses of less than 1% abs for the operating conditions of a tandem cell.
ISSN: 2666-5468
DOI: 10.1016/j.egyai.2023.100299
Schools: School of Electrical and Electronic Engineering 
Rights: © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Files in This Item:
File Description SizeFormat 
1-s2.0-S266654682300071X-main.pdf3.46 MBAdobe PDFThumbnail


Updated on Feb 21, 2024

Page view(s)

Updated on Feb 22, 2024


Updated on Feb 22, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.