Electrostatic Potential Profile Generator for Two-Dimensional Semiconductor Devices

Abstract

As efficiency is one of the bottlenecks of device simulation, we propose to employ deep neural networks to generate two-dimensional electrostatic potential profiles for efficiency. Supervising with previously obtained simulation results for various BJT devices, we train deep neural networks to generate an electrostatic potential profile as an initial guess for a non-equilibrium condition with estimating carrier densities by the frozen field simulation. With the generated potential profiles, we significantly reduce the number of Newton iterations without loss of accuracy.