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.
Z. Zhang et al., "Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction," Proceedings of the 2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility (2019, Sapporo, Japan), pp. 305 - 308, Institute of Electrical and Electronics Engineers (IEEE), Jun 2019.
The definitive version is available at https://doi.org/10.23919/EMCTokyo.2019.8893758
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
Electromagnetic Compatibility (EMC) Laboratory
Keywords and Phrases
Boundary Condition; Deep Learning; Dynamic Simulation; PN Junction; Poisson's Equation
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2019 The Institute of Electronics, Information and Communication Engineer, All rights reserved.
01 Jun 2019