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Project Name: Solution for Predictive Physical Modeling in CdTe and Other Thin-Film PV Technologies
Funding Opportunity: PVRD
SunShot Subprogram: Photovoltaics
Location: Tempe, AZ
SunShot Award Amount: $812,998
Awardee Cost Share: $90,322
Project Investigator: Dragica Vasileska
This project aims to develop a software tool to enable a more accurate interpretation of cadmium telluride (CdTe) thin-film photovoltaic (PV) device performance and material properties, with the goal of enabling predictive device design. The software includes a modeling tool that accounts for atomic diffusion and drift as well as electronic behavior and device performance. This will allow researchers to simulate recombination losses over time in II-VI absorber materials under specified process and stress conditions.
The research team is working to eliminate the ambiguity between the observed device performance and the physical root cause. They will develop several models that account for diffusion and drift at the atomistic level coupled to the electronic subsystem responsible for a PV device’s function. More advanced physical models will describe the capture, emission, and recombination phenomena relevant to multivalent dopants, amphoteric centers, and donor-acceptor pairs. These models will be implemented in a self-contained simulation tool that will drastically reduce interpretation ambiguity and, for the first time, allow for predictive design of thin-film PV devices.
The software tool developed by this project will help to explain and predict performance of thin film PV devices built in a II-VI material system. The software tool will include reaction-diffusion models to accommodate the time and position dependency of II-VI semiconductor alloy stoichiometry and corresponding dependencies of the properties of point defects and complexes. It will also include rate models to describe ionization reactions of electrically active centers in II-VI semiconductors and interactions at interfaces with a high degree of disorder. The software will be able to efficiently solve large systems of diffusion-reaction equations.