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Comparative Analysis of Mobility and Dopant Number Fluctuation Models for the Threshold Voltage Fluctuation Estimation in 45 nm Channel Length MOSFET Device
Published in James E. Morris, Krzysztof Iniewski, Nanoelectronic Device Applications Handbook, 2017
Nabil Ashraf, Dragica Vasileska, Gilson Wirth, Purushothaman Srinivasan
To avoid the problem with the k-space treatment of the Coulomb interaction, a real-space approach has been proposed by Lugli and Ferry [11] in which the electron–electron and the electron–ion interactions are accounted for via real-space molecular dynamics routine. It is important to note that direct application of the real-space molecular dynamics can be used for bulk systems only where it is not required to solve the Poisson equation. Hence, an approach is needed that correctly accounts for the full Coulomb interaction in particle-based device simulators. The group from Arizona State University (ASU) has, in that sense, been a pioneer in this field and in our simulation modules, we have currently implemented three approaches: The corrected Coulomb approach—an approach that we have introduced [12].The particle–particle–particle–mesh coupling method due to Hockney and Eastwood [13].Fast multipole method [14].
Molecular simulations of water and ion transport through nanoporous membranes
Published in Alberto Figoli, Jan Hoinkis, Sacide Alsoy Altinkaya, Jochen Bundschuh, Application of Nanotechnology in Membranes for Water Treatment, 2017
Richard Renou, Minxia Ding, Haochen Zhu, Aziz Ghoufi, Anthony Szymczyk
Long-range charge-charge interactions are difficult to handle in computer simulations because of the limited size of the simulation boxes. Equation (11.9) is, therefore, not practical in molecular simulations and several alternative methods, such as the Ewald summation (Ewald, 1921), the particle mesh Ewald method (Darden et al., 1993) or the smooth particle mesh Ewald method (Essmann et al., 1995), have been developed to optimize the computation of electrostatic interactions.
Molecular dynamics simulations on the adsorption of 4-n-octyl-4′-cyanobiphenyl (8CB) at the air/water interface
Published in Journal of Dispersion Science and Technology, 2018
All MD simulations for interfacial systems were performed using NAMD[20] in NVT ensemble at 309 K in the nematic region of the P8CB. The conjugate-gradient procedure was firstly used to remove bad contacts between atoms. The equations of motions were integrated by the velocity Verlet algorithm. The temperature was controlled via Langevin thermostat. Three-dimensional periodic boundary conditions were applied. The long-range electrostatic interactions were handled with the particle mesh Ewald method. A cut-off distance of 13 Å was applied for both electrostatic and van der Waals interactions. All covalent bonds were constrained at their equilibrium lengths by means of the SHAKE algorithm. During equilibration runs, density profiles, orientational OPs of second rank, and energy convergence were monitored, and no water was observed to penetrate into the vacuum region. Simulation trajectories were visualized using VMD version 1.9.1.[21]
Comparative study of external electric field and potential effects on liquid water ions
Published in Molecular Physics, 2022
Satyanarayana Bonakala, Mohammad I. Hasan
Before applying CPM or CFM to the simulation system, the bulk water system with a density of 1 g cm−3 was equilibrated using constant-NVT simulations at 298 K for 0.5 ns. The temperature was maintained through the use of the Nosé–Hoover thermostat with a damping factor of 100 fs. Bonds and angles in water molecules were constrained using the SHAKE algorithm at every MD step. The equation of motion is integrated using a time step of 1.0 fs. Electrostatic interactions were calculated using the particle–particle particle–mesh (PPPM) method. Long-range corrections to energy and pressure were applied.
Multiscale modelling and splitting approaches for fluids composed of Coulomb-interacting particles
Published in Mathematical and Computer Modelling of Dynamical Systems, 2018
Domain Decomposition (Grid-based): Here, we underlie a spatial domain. Such methods are also known as particle-mesh methods, e.g. particle-in-cell methods (PIC), see [76]. We have to deal with particle and field equations, see [38]. The results of the phase-space formulation are interpolated to the grid formulation and vice versa, see [38]. Such methods decompose the domain, i.e. parallel domain decomposition methods, and are more delicate to parallelize, see [77].