Modeling and sensitivity analysis of complex photovoltaic device processes is explored in this work. We use conditional variational autoencoders to learn the generative model and latent space of the process which is in turn used to predict the device performance. We further compute the Jacobian of the trained neural network to compute global sensitivity indices of the inputs in order to obtain an intuition and interpretation of the process. The results show the outperformance of generative models compared to predictive models for learning device processes. Furthermore, comparison of the results with sampling-based sensitivity analysis methods demonstrates the validity of our approach and the interpretability of the learned latent space.