To reduce the cost of silicon solar cells and improve their efficiency, it is crucial to identify and understand the defects limiting the electrical performance in silicon wafers. Bulk defects in semiconductors produce discrete energy levels within the bandgap and may act as recombination centers. This project investigates the viability of using machine learning for characterizing bulk defects in Silicon by using a Random Forest Regressor to extract the defect energy level and capture cross section ratios for a simulated Molybdenum defect and experimental Silicon Vacancy defect. Additionally, a dual convolutional neural network is used to classify the defect energy level in the upper or lower half bandgap.

The goal of the presented research is using Electro Field-assisted Nano Ink Writing(EF-NIW) to deposit poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, or PEDOT, on a substrate to serve as a basis for designing high-efficiency, scalable solar cells. Through the analysis of parameters that affect electrospray deposition, methods to accurately produce a PEDOT film will be determined. With the finished, contingent film, tests for efficacy can be performed. The film will be analyzed for profilometry, determining the thickness of the film. The film will then be put up to a conductivity test.