Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction
This work addresses a computational bottleneck in semiconductor device simulation, offering a faster alternative for researchers and engineers, though it is incremental as it applies an existing deep learning technique to a specific domain problem.
The paper tackled the time-consuming process of solving Poisson's equation at every time step in PN junction simulations by using deep learning to accelerate it, achieving a perfect match in I-V curves with a 10x speedup compared to the traditional finite difference method.
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.