2020 Volume 17 Issue 4 Pages 20190739
We investigate the application of convolutional neural networks (CNNs) to accelerate quantum mechanical transport simulations (based on the nonequilibrium Green’s function (NEGF) method) of double-gate MOSFETS. In particular, given a potential distribution as input data, we implement the convolutional autoencoder to train and predict the carrier density and local quantum capacitance distributions. The results indicate that the use of a single trained CNN model in the NEGF self-consistent calculation along with Poisson’s equation produces accurate potentials for a wide range of the gate lengths, and all within a significantly shorter computational time than the conventional NEGF calculations.