Title
High-Throughput Quality Inspection of Solar Cells Using Deep Learning Under Consideration of Its Sustainability Impact
Author
Reinhard, Marko (TU Delft Technology, Policy and Management)
Contributor
Santbergen, R. (mentor) 
Blanco, Carlos Felipe (mentor)
Degree granting institution
Delft University of Technology
Universiteit Leiden
Programme
Industrial Ecology
Date
2022-08-31
Abstract
To meet global market demands, it will remain important to further scale up photovoltaics (PV) production. During the production of solar cells, several defects can occur. Current approaches in quality inspection are reaching their speed limits. This thesis project evaluates the feasibility of faster quality inspection by using deep learning-based computer vision (CV) algorithms to detect production defects without human supervision at high speeds. The goal is to achieve this while reducing the necessary manual efforts to label (annotate) defects in the training data of such algorithms.
The second goal of the project is to investigate in which ways and to which extent this innovation can impact the sustainability performance of the solar cell production process. Multiple scenarios are investigated using a Life Cycle Assessment (LCA) model. The results are used to estimate the potential large-scale impact of increasing solar cell production throughput.
Subject
Photovoltaics
Deep Learning
Computer Vision
Sustainability
Electroluminescence
quality Inspection
Environmental impact
To reference this document use:
http://resolver.tudelft.nl/uuid:e30c14c6-46f9-4941-b992-879d62ffa542
Embargo date
2023-10-01
Part of collection
Student theses
Document type
master thesis
Rights
© 2022 Marko Reinhard