Benjamin P Haley

Atomistic Simulation of Realistically Sized Nanodevices Using NEMO 3-D—Part I: Models and Benchmarks

Device physics and material science meet at the atomic scale of novel nanostructured semiconductors, and the distinction between new device or new material is blurred. Not only the quantum-mechanical effects in the electronic states of the device but also the granular atomistic representation of the underlying material are important. Approaches based on a continuum representation of the underlying material typically used by device engineers and physicists become invalid. Ab initio methods used by material scientists typically do not represent the band gaps and masses precisely enough for device design, or they do not scale to realistically large device sizes. The plethora of geometry, material, and doping configurations in semiconductor devices at the nanoscale suggests that a general nanoelectronic modeling tool is needed. The 3-D NanoElectronic MOdeling (NEMO 3-D) tool has been developed to address these needs. Based on the atomistic valence force field and a variety of nearest neighbor tight-binding models (e.g., s, sp3s*, and sp3d5s*), NEMO 3-D enables the computation of strain and electronic structure for more than 64 and 52 million atoms, corresponding to volumes of (110 nm)3 and (101 nm)3, respectively. The physical problem may involve very large scale computations, and NEMO 3-D has been designed and optimized to be scalable from single central processing units to large numbers of processors on commodity clusters and supercomputers. NEMO 3-D has been released with an open-source license in 2003 and is continually developed by the Network for Computational Nanotechnology (NCN). A web-based online interactive version for educational purposes is freely available on the NCN portal ( http://www.nanoHUB.org). In this paper, theoretical models and essential algorithmic and computational components that have been used in the development and successful deployment of NEMO 3-D are discussed.

Advancing nanoelectronic device modeling through peta-scale computing and deployment on nanoHUB

Recent improvements to existing HPC codes NEMO 3-D and OMEN, combined with access to peta-scale computing resources, have enabled realistic device engineering simulations that were previously infeasible. NEMO 3-D can now simulate 1 billion atom systems, and, using 3D spatial decomposition, scale to 32768 cores. Simulation time for the band structure of an experimental P doped Si quantum computing device fell from 40 minutes to 1 minute. OMEN can perform fully quantum mechanical transport calculations for real-word UTB FETs on 147,456 cores in roughly 5 minutes. Both of these tools power simulation engines on the nanoHUB, giving the community access to previously unavailable research capabilities.

PUQ: A code for non-intrusive uncertainty propagation in computer simulations

Abstract We present a software package for the non-intrusive propagation of uncertainties in input parameters through computer simulation codes or mathematical models and associated analysis; we demonstrate its use to drive micromechanical simulations using a phase field approach to dislocation dynamics. The PRISM uncertainty quantification framework (PUQ) offers several methods to sample the distribution of input variables and to obtain surrogate models (or response functions) that relate the uncertain inputs with the quantities of interest (QoIs); the surrogate models are ultimately used to propagate uncertainties. PUQ requires minimal changes in the simulation code, just those required to annotate the QoI(s) for its analysis. Collocation methods include Monte Carlo, Latin Hypercube and Smolyak sparse grids and surrogate models can be obtained in terms of radial basis functions and via generalized polynomial chaos. PUQ uses the method of elementary effects for sensitivity analysis in Smolyak runs. The code is available for download and also available for cloud computing in nanoHUB. PUQ orchestrates runs of the nanoPLASTICITY tool at nanoHUB where users can propagate uncertainties in dislocation dynamics simulations using simply a web browser, without downloading or installing any software. Program summary Program title: PUQ Catalogue identifier: AEWP_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEWP_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: MIT license No. of lines in distributed program, including test data, etc.: 45075 No. of bytes in distributed program, including test data, etc.: 3318862 Distribution format: tar.gz Programming language: Python, C. Computer: Workstations. Operating system: Linux, Mac OSX. Classification: 4.11, 4.12, 4.13. External routines: SciPy, Matplotlib, h5py Nature of problem: Uncertainty propagation and creation of response surfaces. Solution method: Generalized Polynomial Chaos (gPC) using Smolyak sparse grids. Running time: PUQ performs uncertainty quantification and sensitivity analysis by running a simulation multiple times using different values for input parameters. Its run time will be the product of the run time of the chosen simulation code and the number of runs required to achieve the desired accuracy.

Vacancy clustering and diffusion in silicon: Kinetic lattice Monte Carlo simulations

Diffusion and clustering of lattice vacancies in silicon as a function of temperature, concentration, and interaction range are investigated by Kinetic Lattice Monte Carlo simulations. It is found that higher temperatures lead to larger clusters with shorter lifetimes on average, which grow by attracting free vacancies, while clusters at lower temperatures grow by aggregation of smaller clusters. Long interaction ranges produce enhanced diffusivity and fewer clusters. Greater vacancy concentrations lead to more clusters, with fewer free vacancies, but the size of the clusters is largely independent of concentration. Vacancy diffusivity is shown to obey power law behavior over time, and the exponent of this law is shown to increase with concentration, at fixed temperature, and decrease with temperature, at fixed concentration.

Vacancy-assisted arsenic diffusion and time-dependent clustering effects in silicon

We present results of kinetic lattice Monte Carlo (KLMC) simulations of substitutional arsenic diffusion in silicon mediated by lattice vacancies. Large systems are considered, with 1000 dopant atoms and long range \textit{ab initio} interactions, to the 18th nearest lattice neighbor, and the diffusivity of each defect species over time is calculated. The concentration of vacancies is greater than equilibrium concentrations in order to simulate conditions shortly after ion implantation. A previously unreported time dependence in the applicability of the pair diffusion model, even at low temperatures, is demonstrated. Additionally, long range interactions are shown to be of critical importance in KLMC simulations; when shorter interaction ranges are considered only clusters composed entirely of vacancies form. An increase in arsenic diffusivity for arsenic concentrations up to

The nanoelectronic modeling tool NEMO 5: Capabilities, validation, and application to Sb-heterostructures

10^{19} \text{cm}^{-3}

Molecular Structure of PAN-based Carbon Fiber Precursor

is observed, along with a decrease in arsenic diffusivity for higher arsenic concentrations, due to the formation of arsenic dominated clusters. Finally, the effect of vacancy concentration on diffusivity and clustering is studied, and increasing vacancy concentration is found to lead to a greater number of clusters, more defects per cluster, and a greater vacancy fraction within the clusters.

Multimillion Atom Simulations with NEMO 3-D

Modeling and simulation take an important role in the exploration and design optimization of novel devices. As the downscaling of electronic devices continues, the description of interfaces, randomness, and disorder on an atomistic level gains importance and continuum descriptions lose their validity. Often a full-band description of the electronic structure is needed to model the interaction of different valleys and nonparabolicity effects. NEMO 5 [1] is a modeling tool that addresses these issues and is able to provide insight into a broad range of devices. It unifies the capabilities of prior projects: multiscale approaches to quantum transport in planar structures in NEMO-1D [2], multimillion-atom simulations of strain and electronic structure in NEMO-3D [3] and NEMO-3D-Peta [4], and quantum transport in nonplanar structures in OMEN [5]. NEMO 5 aims at becoming a community code whose structure, implementation, resource requirements and license allow experimental and theoretical researchers in academia and industry alike to use and extend the tool.

Computational Nanoelectronics Research and Education at nanoHUB.org

The molecular structure of the Polyacrylonitrile(PAN) precursor is known to critically affect the microstructure, and consequently properties, of the final carbon fibers (CFs). Despite the importance of PAN-based carbon fibers, molecular modeling has not been attempted on these precursors and the only structural information available is from models proposed based on XRD experiments. We use molecular modeling to predict the molecular structure of spun PAN fiber precursors and their properties. Our goal is to predict low energy, fully relaxed structures and compare the resulting structures with experiments. The first step in the procedure is to generate a family of helical, rod-like isotactic and atactic isolated chain structures by building chains with a fixed torsional angle ranging from 5 to 175. The second step is to pack the individual chains into a simulation cell following a closed-packed hexagonal arrangement and fully relax the cells using isobaric and isotherm molecular dynamics (MD) simulations. We find that chains built using torsional angles between 120 and 145 result in the lowest energy structures in the condensed phase. Furthermore, the simulated structure factors showed excellent agreement with experimental data in both peak positions and relative intensities.