Effects of dimensionality and spatial distribution on the magnetic relaxation of interacting ferromagnetic nanoclusters: A Monte Carlo study

2014 ◽  
Vol 115 (17) ◽  
pp. 173906 ◽  
Author(s):  
D. Brinis ◽  
A. Laggoun ◽  
D. Ledue ◽  
R. Patte
Keyword(s):  
Monte Carlo ◽  
Spatial Distribution ◽  
Magnetic Relaxation ◽  
Monte Carlo Study

scholarly journals Dipolar interaction and incoherent quantum tunneling: a Monte Carlo study of magnetic relaxation

1999 ◽  
Vol 12 (1) ◽  
pp. 39-46 ◽  
Author(s):  
A. Cuccoli ◽  
A. Fort ◽  
A. Rettori ◽  
E. Adam ◽  
J. Villain
Keyword(s):  
Monte Carlo ◽  
Quantum Tunneling ◽  
Magnetic Relaxation ◽  
Monte Carlo Study ◽  
Dipolar Interaction

scholarly journals Monte Carlo Study of the Lateral Distribution of Gate Current Density Along the Channel of Submicron LDD MOSFET's

VLSI Design ◽  
2001 ◽  
Vol 13 (1-4) ◽  
pp. 301-304 ◽  
Author(s):  
A. Harkar ◽  
R. W. Kelsall ◽  
J. N. Ellis
Keyword(s):  
Monte Carlo ◽  
Spatial Distribution ◽  
Current Density ◽  
Monte Carlo Study ◽  
Hot Electrons ◽  
Gate Current ◽  
Enhancement Technique ◽  
Drain Side ◽  
Full Band ◽  
Monte Carlo Simulator

This paper presents the spatial distribution of hot electrons along the channel of a 0.35 micron MOSFET using the full band pseudopotential Monte Carlo simulator DAMOCLES. The important aspect of this investigation is the implementation of a rigorous statistical enhancement technique along the channel of the device, to probe the hot electrons generated along the channel. Simulations have been carried out for different bias points. The results clearly show that the probability of generating electrons with energies sufficient for injection into the oxide layer is significant only at the drain side of the device. It is also observed that the peak gate current occurs exactly just inside the LDD region coincident with the position of maximum lateral electric field.

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

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