Presenter Profile
Kristopher O. Davis
Associate Professor
University of Central Florida, Department of Materials Science and Engineering
Prof. Kristopher O. Davis is an Associate Professor in Materials Science and Engineering at the University of Central Florida (UCF) with secondary joint appointments with the Resilient, Intelligent and Sustainable Energy Systems (RISES) Center, Florida Solar Energy Center (FSEC), and CREOL, the College of Optics and Photonics. He served as the c-Si Metrology Program Manager of the U.S. Photovoltaic Manufacturing Consortium (PVMC) from 2012-2016 and has supported twelve U.S. Dept. of Energy (DOE) projects as PI, Co-PI, or a subrecipient. In the past ten years, Prof. Davis has published over 50 peer-reviewed journal publications on a wide range of PV topics and previously served on the IEEE Photovoltaic Device Committee.
TALK TITLE
Advanced Characterization and Multimodal Data Analysis in the Photovoltaics Sector
KEYWORDS
photovoltaics, multimodal data analysis, semiconductors, optoelectronics, electronics
ABSTRACT
Photovoltaic (PV) cells and modules are large area devices meant to collect light over large swaths of area and convert it into electricity for consumer use. The economic viability of PV technologies depends on a combination of the performance (i.e., efficiency), manufacturing cost, and the long-term reliability and durability of these products. Unfortunately, very small defects on the micron, and even nanometer, scale can dramatically impact the performance of PV cells and modules. These defects can be formed during manufacturing or during field operation, and it is therefore critical to quickly detect, locate, and classify these issues in both a production environment and in the field. This presentation will show how state-of-the-art characterization methods combined with multimodal data analysis can be used to improve manufacturing processes and better understand degradation mechanisms in PV cells and modules. Examples featuring both simple statistical models as well as more complex deep learning models are provided to highlight the benefits of this approach. Finally, future areas of opportunity for data science in the PV sector will be shared with the hope of stimulating future collaborations amongst attendees of the symposium