  Simulating the dynamic characteristics of a PN junction at the microscopic
level requires solving the Poisson's equation at every time step. Solving at
every time step is a necessary but time-consuming process when using the
traditional finite difference (FDM) approach. Deep learning is a powerful
technique to fit complex functions. In this work, deep learning is utilized to
accelerate solving Poisson's equation in a PN junction. The role of the
boundary condition is emphasized in the loss function to ensure a better
fitting. The resulting I-V curve for the PN junction, using the deep learning
solver presented in this work, shows a perfect match to the I-V curve obtained
using the finite difference method, with the advantage of being 10 times faster
at every time step.
