Optimization of Silicon Solar Cell Efficiency
- DOI
- 10.2991/978-94-6463-787-8_41How to use a DOI?
- Keywords
- SILVACO TCAD; Silicon Solar cell; Renewable Energy; ATLAS Simulation
- Abstract
This paper presents a detailed performance analysis of silicon solar cells using the SILVACO TCAD software. Silicon, a widely used material in photovoltaic technology, is known for its durability, efficiency, and low maintenance. This simulation utilizes precise mesh generation through similar X and Y mesh parameters providing accurate modeling of critical device regions. SILVACO TCAD offers a comprehensive platform for designing solar cell structures, specifying material properties, and including advanced physical models, such as Shockley-Read-Hall recombination and Auger recombination. Under typical AM1.5 lighting circumstances, key performance indicators such as consistency, filling factor, no-load voltage, and maximum current density are retrieved. And spectral response of the device is analyzed by evaluating the internal photoelectric efficiency and spectral responsive in terms of photon count across a range in wavelengths. This study highlights the importance of simulation tools in analyzing photovoltaic device performance and advancing the development of efficient solar technologies.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Rajan Singh AU - T. Viswanjali AU - S. Durga AU - Praveen Yadav AU - K. Vignesh AU - Shrikant Upadhyay PY - 2025 DA - 2025/07/17 TI - Optimization of Silicon Solar Cell Efficiency BT - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025) PB - Atlantis Press SP - 524 EP - 531 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-787-8_41 DO - 10.2991/978-94-6463-787-8_41 ID - Singh2025 ER -