Summary
2019 Joint International Symposium on Electromagnetic Compatibility and Asia-Pacific International Symposium on Electromagnetic Compatibility, Sapporo
2019
Session Number:WedAM1C
Session:
Number:WedAM1C.1
Solving Poisson?fs Equation Using Deep Learning in Particle Simulation of PN Junction
Zhongyang Zhang, Ling Zhang, Ze Sun, Nicholas Erickson, Ryan From, Jun Fan,
pp.-
Publication Date:2016/10/5
Online ISSN:2188-5079
DOI:10.34385/proc.58.WedAM1C.1
Summary:
Simulating the dynamic characteristics of a PN junction at the microscopic level requires solving the Poisson?fs 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?fs 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.