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.
@article{arxiv.1810.10192,
title = {Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction},
author = {Zhongyang Zhang and Ling Zhang and Ze Sun and Nicholas Erickson and Ryan From and Jun Fan},
journal= {arXiv preprint arXiv:1810.10192},
year = {2018}
}