Deep Learning Based Poisson Solver in Particle Simulation of PN Junction with Transient ESD Excitation

Abstract

In particle simulations of semiconductor devices for electro-static discharge (ESD) study at the microscopic level, solving Poisson's equation is an inevitable but time-consuming step. In this work, a deep learning technique is utilized to resolve Poisson's equation for a PN junction under an ESD event, namely using a trained deep neural network (DNN) to predict the potential distribution according to the charge distribution and the boundary condition under a transient ESD excitation. To improve the generalization performance of the DNN, multiple typical ESD curves with different parameters are used as the excitation boundary to generate large amounts of training data with a finite-element method (FEM) solver. After being trained, the DNN is used in the particle simulation to calculate the current response of the PN junction to a new ESD voltage curve that has never been trained before, and the result can perfectly match with that obtained from the FEM solver.