Electrostatic Image Theory for an Anisotropic Boundary of an Anisotropic Half-Space

Hänninen, Jari J.; Lindell, Ismo V.; Nikoskinen, K.

doi:10.2528/pier03122201

Cited by 5 publications

(3 citation statements)

References 7 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we have a set of metallic objects that are connected to a battery that provides a constant electric potential. The calculation of electric magnitudes such as the electrostatic potential or the force between these elements is, in general, very difficult, and only a few specific geometries can be analytically calculated [ 13 ]. To solve electrostatic problems with arbitrary geometries, an algorithm called the generalized image charge method [ 14 , 15 ] (GICM) has been developed.…”

Section: Methodsmentioning

confidence: 99%

The use of artificial neural networks in electrostatic force microscopy

et al. 2012

View full text Add to dashboard Cite

The use of electrostatic force microscopy (EFM) to characterize and manipulate surfaces at the nanoscale usually faces the problem of dealing with systems where several parameters are not known. Artificial neural networks (ANNs) have demonstrated to be a very useful tool to tackle this type of problems. Here, we show that the use of ANNs allows us to quantitatively estimate magnitudes such as the dielectric constant of thin films. To improve thin film dielectric constant estimations in EFM, we first increase the accuracy of numerical simulations by replacing the standard minimization technique by a method based on ANN learning algorithms. Second, we use the improved numerical results to build a complete training set for a new ANN. The results obtained by the ANN suggest that accurate values for the thin film dielectric constant can only be estimated if the thin film thickness and sample dielectric constant are known.PACS: 07.79.Lh; 07.05.Mh; 61.46.Fg.

show abstract

Section: Methodsmentioning

confidence: 99%

The use of artificial neural networks in electrostatic force microscopy

et al. 2012

View full text Add to dashboard Cite

show abstract

“…We use the cross-coefficients calculator which is served by Hänninen [5]. We can take into account temperature and pressure dependence of the cross-coefficients by thanks of this calculator.…”

Section: Modelmentioning

confidence: 99%

Textures and NMR spectra of a superfluid 3He-A phase confined in rotating narrow cylinders

Takagi

2005

Journal of Physics and Chemistry of Solids

View full text Add to dashboard Cite

We confine a superfluid 3 He-A phase in narrow cylinders, these radii are 0.05 and 0.1 mm. The system is rotated around the cylinder axis, and its rotation velocity ranges between K6.28 and 6.28 rad/s. The strong magnetic field (22 mT) is applied along cylinder axis. In these systems, we determined l-and d-textures by minimizing GL-free energies. In the case of the small cylinder (RZ0.05 mm) a dipole locked and a dipole unlocked Mermin-Ho textures are obtained as local minimum free energy states. In the case of the large cylinder (RZ0.1 mm), a single vortex and a three vortices states are obtained. The stability of each state depends on the rotating velocity. A vortex charge change of two quanta occurs by a pair of continuous vortices coming in and going out. For each case, the NMR spectrum of the transverse resonance mode is calculated by using a finite element method (FEM). Using the FEM the boundary condition of spin wave mode is strictly imposed and the higher resonance mode is also calculated. The calculated result agrees well with empirical data. q

show abstract

“…The dielectric sample is included by the Image Charge theory [13,14]. Although the current version of the algorithm is used for isotropic samples, it can be easily generalized for anisotropic ones using the formalism developed by J. J. Hänninen et al [15]. This small amount of charges plays the role of the network units weighted by the absolute value of the charges q i (obtained after a standard least-squares minimization of the electrostatic potential at the tip surface).…”

Section: Introductionmentioning

confidence: 99%

Generalized Image Charge Method to Calculate Electrostatic Magnitudes at the Nanoscale Powered by Artificial Neural Networks

Sacha

Rodríguez

Serrano

et al. 2010

Journal of Electromagnetic Waves and Applications

View full text Add to dashboard Cite

A technique to calculate electrostatic magnitudes such as force and potential in Electrostatic Force Microscopy setups is presented. This technique combines Artificial Neural Networks and the Generalized Image Charge Method to overcome one of the main problems of traditional numerical simulations: the need of many parameters that are difficult to estimate and depend on the geometry of the experimental setup. Using Artificial Neural Networks, our technique is able to estimate the internal parameters of the algorithm and automatically obtain the electric magnitudes with a very high accuracy. This technique has been implemented in the freely distributed software winGICM. The automatic configuration of the software by an Artificial Neural Network allows the users to handle it without being specifically trained in the theoretical background underlying the algorithms.

show abstract

Electrostatic Image Theory for an Anisotropic Boundary of an Anisotropic Half-Space

Cited by 5 publications

References 7 publications

The use of artificial neural networks in electrostatic force microscopy

The use of artificial neural networks in electrostatic force microscopy

Textures and NMR spectra of a superfluid 3He-A phase confined in rotating narrow cylinders

Generalized Image Charge Method to Calculate Electrostatic Magnitudes at the Nanoscale Powered by Artificial Neural Networks

Contact Info

Product

Resources

About