Modified GRNN based atomic modeling approach for nanoscale devices and TFET implementation

Abstract

The simulation of realistic device models in quantum transport requires an extreme amount of memory and computation time. The computational burden in quantum transport is caused by the recursive numerical solution requirement of the Schrödinger equation with non-equilibrium Green’s function formalism. Ever decreasing device size increases the domination of the quantum mechanical effects such as scattering. Considerations of the quantum mechanical effects are crucial for emerging nanoscale devices. The solutions must consider the interactions between electron-electron, electron-phonon for qualified device modeling. In this work, a modified version of General Regression Neural Network and Non-Equilibrium Green’s Function hybrid modeling approach is proposed to overcome the mentioned computational burden. Through proposed computation processing, a pattern layer node is assigned for each atom in the atomic layer. In modified GRNN, pattern layer neuron values were extracted from NEGF calculation of three atomic layers. Higher atomic layer potential function calculation for Schrödinger equation is estimated by modified GRNN with dynamic pattern layer extension ability. A regression neuron is added to the output of the modified GRNN. Proposed modified GRNN topology is applied to model and solve atomic layer potential functions of Tunnel Field Effect Transistor in the range of seven to twenty-three atomic layers. Each atomic layer contains a hundred atoms in a row. Training data are obtained from the first three atomic layers of Tunnel FET. These training data are used for the estimation of test results for seven to twenty-three atomic layers. Results are compared with that of the incoherent NEGF model of Datta. Approximately 40 % simulation convergence time decrease is observed during implementations. Simulation results proved the importance and efficiency of the proposed approach.