scholarly journals Ultrahigh dielectric constant of thin films obtained by electrostatic force microscopy and artificial neural networks

2012 ◽  
Vol 100 (2) ◽  
pp. 023101 ◽  
Author(s):  
E. Castellano-Hernández ◽  
G. M. Sacha
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Dielectric Constant ◽  
Artificial Neural Networks ◽  
Electrostatic Force ◽  
Electrostatic Force Microscopy ◽  
Force Microscopy ◽  
Artificial Neural

scholarly journals The use of artificial neural networks in electrostatic force microscopy

2012 ◽  
Vol 7 (1) ◽  
pp. 250 ◽  
Author(s):  
Elena Castellano-Hernández ◽  
Francisco B Rodríguez ◽  
Eduardo Serrano ◽  
Pablo Varona ◽  
Gomez Sacha
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Electrostatic Force ◽  
Electrostatic Force Microscopy ◽  
Force Microscopy ◽  
Artificial Neural

scholarly journals Electrostatic force microscopy analysis of Bi0.7Dy0.3FeO3 thin films prepared by pulsed laser deposition integrated with ZnO films for microelectromechanical systems and memory applications

2018 ◽  
Vol 4 (2) ◽  
pp. 77-85
Author(s):  
Deepak Bhatia ◽  
Sandipta Roy ◽  
S. Nawaz ◽  
R.S. Meena ◽  
V.R. Palkar
Keyword(s):  
Thin Films ◽  
Electrical Transport ◽  
Microelectromechanical Systems ◽  
Electrostatic Force ◽  
Charge Trapping ◽  
Electrostatic Force Microscopy ◽  
Zno Films ◽  
Phase Dependence ◽  
Force Microscopy ◽  
Electric Charge Distribution

In this paper, we report the charge trapping phenomena in zinc oxide (n-ZnO) and Bi0.7Dy0.3FeO3 (BDFO)/ZnO thin films deposited on p-type <100> conducting Si substrate. The significant change in contrast above the protrusions of ZnO verifies the possibility of heavy accumulation of injected holes in there. The ZnO and BDFO/ZnO films were characterized by the electrostatic force microscopy (EFM) to understand the phase dependence phenomenon on the bias supporting electron tunnelling. The EFM has an important role in the analysis of electrical transport mechanism characterization and electric charge distribution of local surface in nanoscale devices. It was observed that in BDFO/ZnO, the contrast of EFM images remains constant with the bias switching and that primarily indicates availability of trap sites to host electrons. The change in contrast over the protrusions of ZnO suggests that mobility of the electrical charge carriers may be through the grain boundary. The formation of these hole-trapped sites may be assumed by bond breaking phenomenon.

Artificial Neural Networks Estimation for Thicknesses of Multilayer Nano-Scale Films

2019 ◽  
Vol 962 ◽  
pp. 41-48
Author(s):  
Tzong Daw Wu ◽  
Jiun Shen Chen ◽  
Ching Pei Tseng ◽  
Cheng Chang Hsieh
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Film Growth ◽  
Mean Squared Error ◽  
Squared Error ◽  
Thin Film Optics ◽  
The Mean ◽  
Artificial Neural

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.

Application of Artificial Neural Networks to Predict Chemical Composition of Electrodeposited Nanocrystalline Ni-Mo Thin Films

2013 ◽  
Vol 50 (52) ◽  
pp. 63-71
Author(s):  
M. H. Allahyarzadeh ◽  
A. Ashrafi ◽  
T. Shahrabi ◽  
A. Seddighian ◽  
M. Aliofkhazraei ◽  
...  
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Artificial Neural

scholarly journals Mapping the local dielectric constant of a bio‑nanostructured system

2020 ◽  
Author(s):  
Wescley Walison Valeriano ◽  
Rodrigo Ribeiro Andrade ◽  
Juan Pablo Vasco ◽  
Angelo Malachias ◽  
Bernardo Ruegger Almeida Neves ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Cross Section ◽  
Structural Information ◽  
Electrostatic Force ◽  
Electrostatic Force Microscopy ◽  
Visible Range ◽  
Force Microscopy ◽  
Measured Profile ◽  
Amazonian Rainforest ◽  
Reflectance Measurements

  The aim of this work is to determine the dielectric constant value of a bio-nanostructured system via Electrostatic Force Microscopy (EFM) and to show how this method is useful to study natural photonic crystals. We mapped the dielectric constant of the cross-section of the posterior wing of the damselfly Chalcopteryx rutilans with nanometric resolution and obtained not only structural information on its constitutive nanolayers but also on the absolute values of the dielectric constant variation in a nanometric scale. By relating the measured profile of the static dielectric constant to the profile of the refractive index in the visible range, combined with optical reflectance measurements and simulation, we were able to describe the origin of the strongly iridescent wing colors of this Amazonian rainforest damselfly. The method we demonstrate here should be useful for the study of other nanostructured biological systems.

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