90006980

open access

Version of Record

Acceleration of nonequilibrium Green's function simulation for nanoscale FETs by applying convolutional neural network model

English (英語)

IEICE Electronics Express

17(4)

20190739-20190739

Institute of Electronics, Information and Communication Engineers(IEICE)

2020

2020-04-01

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.

convolutional autoencoder

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