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Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction by Zhang, Zhongyang; Zhang, Ling; Sun, Ze; Erickson, Nicholas; From, Ryan; Fan, Jun is a scholarly article available to read on EtoBox.

What is Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction about?

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.

Author

Zhang, Zhongyang; Zhang, Ling; Sun, Ze; Erickson, Nicholas; From, Ryan; Fan, Jun

Published

2018

Language

EN