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ESROP – Exploring Metallization of Tandem Solar Cells through Machine Learning and Simulations
Tandem solar cells represent the future of renewable solar energy, offering efficiencies beyond traditional single-junction silicon cells. However, their current commercial viability is limited by their metallization pattern, which is the silver contacts on the surface that collect electricity. Current design processes are manual and computationally expensive; therefore, this project aims to integrate machine learning to optimize the design of these patterns. By developing an algorithm in Python and Griddler software, we aim to optimize both the power conversion efficiency and production costs of the tandem solar cells, accelerating the global transition to clean, renewable solar technology.
Faculty Supervisor:
Arthur Chan
Student:
Partner:
National University of Singapore
Discipline:
Engineering
Sector:
Education
University:
University of Toronto
Program:
Globalink Research Award
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.