Machine Learning for Optimal Copper Doping Profile Design in CdTe Solar Cells

In this work we use machine learning to extract actual Cu doping profiles that result from the process of diffusion annealing and cool-down in the fabrication sequence of CdTe solar cells. We use two deep learning neural network models (Artificial Neural Network (ANN) model using a Keras API with TensorFlow backend and a Radial Basis Function Network (RBFN) model) to predict the Cu doping profiles for different temperatures and duration of the annealing process. We find excellent agreement between the simulated results obtained with the PVRD-FASP Solver and predicted values. It takes significant amount of time to generate with the PVRD-FASP Solver the Cu doping profiles given the initial conditions. The generation of the same with machine learning is almost instantaneous and can serve as an excellent simulation tool to guide future fabrication of optimal doping profiles in CdTe solar cells.