Passivating contact-based tunnel junction Si solar cells using machine learning for tandem cell applications

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 rese...

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Main Authors: Park, Hyunjung, Morisset, Audrey, Kim, Munho, Lee, Hae-Seok, Hessler-Wyser, Aïcha, Haug, Franz-Josef, Ballif, Christophe
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/173531
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1735312024-02-16T15:39:04Z Passivating contact-based tunnel junction Si solar cells using machine learning for tandem cell applications Park, Hyunjung Morisset, Audrey Kim, Munho Lee, Hae-Seok Hessler-Wyser, Aïcha Haug, Franz-Josef Ballif, Christophe School of Electrical and Electronic Engineering Engineering Tunnel Junction Passivating Contact 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. Ministry of Education (MOE) Published version This research was funded by the New & Renewable Energy Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), and supported by the ministry of Trade, Industry, Energy, of the Republic of Korea (No. 20204010600470). Munho Kim acknowledges the support of Ministry of Education, Singapore, under AcRF Tier 2 (T2EP50120-0001). We acknowledge additional support through the visiting scientist program of the Institute of Electrical and Microengineering (EPFL-IEM). 2024-02-13T02:24:07Z 2024-02-13T02:24:07Z 2023 Journal Article 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-. https://dx.doi.org/10.1016/j.egyai.2023.100299 2666-5468 https://hdl.handle.net/10356/173531 10.1016/j.egyai.2023.100299 2-s2.0-85170420817 14 100299 en T2EP50120-0001 Energy and AI © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Tunnel Junction
Passivating Contact
spellingShingle Engineering
Tunnel Junction
Passivating Contact
Park, Hyunjung
Morisset, Audrey
Kim, Munho
Lee, Hae-Seok
Hessler-Wyser, Aïcha
Haug, Franz-Josef
Ballif, Christophe
Passivating contact-based tunnel junction Si solar cells using machine learning for tandem cell applications
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Park, Hyunjung
Morisset, Audrey
Kim, Munho
Lee, Hae-Seok
Hessler-Wyser, Aïcha
Haug, Franz-Josef
Ballif, Christophe
format Article
author Park, Hyunjung
Morisset, Audrey
Kim, Munho
Lee, Hae-Seok
Hessler-Wyser, Aïcha
Haug, Franz-Josef
Ballif, Christophe
author_sort Park, Hyunjung
title Passivating contact-based tunnel junction Si solar cells using machine learning for tandem cell applications
title_short Passivating contact-based tunnel junction Si solar cells using machine learning for tandem cell applications
title_full Passivating contact-based tunnel junction Si solar cells using machine learning for tandem cell applications
title_fullStr Passivating contact-based tunnel junction Si solar cells using machine learning for tandem cell applications
title_full_unstemmed Passivating contact-based tunnel junction Si solar cells using machine learning for tandem cell applications
title_sort passivating contact-based tunnel junction si solar cells using machine learning for tandem cell applications
publishDate 2024
url https://hdl.handle.net/10356/173531
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