2018/10/24 by Zhongyang Zhang, Ling Zhang, Zhang, Zhongyang +9
Engineering · #Advancements in Semiconductor Devices and Circuit Design #Artificial Intelligence (cs.AI) #Computational Physics (physics.comp-ph) #Electromagnetic Simulation and Numerical Methods #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Non-Destructive Testing Techniques #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1810.10192
openalex publication_date 2018/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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.