Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction


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.

Meeting Name

2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility, EMC Sapporo/APEMC 2019 (2019:Jun. 3-7, Sapporo, Japan)


Electrical and Computer Engineering

Research Center/Lab(s)

Electromagnetic Compatibility (EMC) Laboratory

Keywords and Phrases

Boundary Condition; Deep Learning; Dynamic Simulation; PN Junction; Poisson's Equation

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2019 The Institute of Electronics, Information and Communication Engineer, All rights reserved.

Publication Date

01 Jun 2019