6533b86dfe1ef96bd12c936b

RESEARCH PRODUCT

A neural network-based approach to determine FDTD eigenfunctions in quantum devices

Antonio SorianoJaume SeguraGh. Tudor DimaEnrique A. Navarro

subject

Artificial neural networkComputer scienceFinite-difference time-domain methodEigenfunctionCondensed Matter PhysicsAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic MaterialsLeast mean squares filtersymbols.namesakeFourier transformConvergence (routing)symbolsElectronic engineeringApplied mathematicsElectrical and Electronic EngineeringQuantumMicrowave

description

This article combines a Neural Network (NN) algorithm with the Finite Difference Time Domain (FDTD) technique to estimate the eigenfunctions in quantum devices. A NN based on the Least Mean Squares (LMS) algorithm is combined with the FDTD technique to provide a first approach to the confined states in quantum wires. The proposed technique is in good agreement with analytical results and is more efficient than FDTD combined with the Fourier Transform. This technique is used to cal- culate a numerical approximation to the eigenfunctions associated to quan- tum wire potentials. The performance and convergence of the proposed technique are also presented in this article. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 2017-2022, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop. 24562

yearjournalcountryeditionlanguage
2009-06-19Microwave and Optical Technology Letters
https://doi.org/10.1002/mop.24562