A Network for
Computational NanotechnologyMark Lundstrom
Electrical and Computer Engineering
Purdue University
Supported by the National Science Foundation, Indiana’s 21st Century Res. and Tech. Fund,and the ARO DURINT program
1. NSF’s Nanoscale Modeling and Simulation Program
2. The nanoHUB
3. The Network for Computational Nanotechnology
Purdue University
Nanoscale Modeling and Simulation
1. Nanoengineered materials (Balazas, et al., Pittsburg)
2. Patterned Magnetic Nanostructures (Clemens, et al, Stanford)
3. Nanoscale Film Morphology (Rahman, et al., Kansas State)
4. Nanostructured Membranes (Wagner, et al. Deleware)
5. Biomolecules in Microfluidic Devices (De Pablo,et al. Wisconsin)
6. Quantum Computation (Lloyd, et al., MIT)
7. Molecular Electronics (Lundstrom, et al. Purdue)
Purdue University
B. Clemens, K. Cho, D. Chrzan, H. Gao, W. NixStanford University and U.C. Berkeley
Goals:Develop a predictive nanostructure patterningmethod using multiscale modeling (quantum, atomistic and continuum models) and apply to magnetic nanostructures as a prototype system with critical experimental validation
DNA flowing through 8m channel(Courtesy of D. C. Schwartz)
Accomplishments:• Ab initio study of metal surface kinetics as a function of surface strains • Strain-dependant kinetic Monte Carlo simulation of nanostructure patterned growth• Identification of micro-structure patterning as nanostructure control technology
Patterned Magnetic Nanostructures
KMC
Ab initio
Purdue University
J. J. de Pablo M.D. Graham University of Wisconsin-Madison
Motivation:Emerging nanoscale technologies, suchas biodetection /microseparation / DNAsequencing require predictive modelingtools for rational design of single-moleculeflows in devices where molecular and devicesizes are comparable
1-5 nm100 nm-1 m
m
DNA flowing through 8m channel(Courtesy of D. C. Schwartz)
Accomplishments:• first predictive model of flowing DNA solutions in a micron-scale channel• first computations of diffusion and flow behavior in the channelOngoing work:• transport of DNA through nanopores• experimental validation of model• application to single-molecule sequencing• flow-enhanced, directed ligations
Biomolecules in Microfluidic Devices
Vision: tools and principles for in silico rational design of biomolecular processes
Purdue University
Norman Wagner, Stanley Sandler Raul Lobo, Douglas DorenUniversity of Delaware
Henry Foley (PSU)Goal:Develop a predictive, coherent theoretical description of configurational diffusion from first principles. A novel, hierarchical approach will connect ab initio quantum mechanical calculations to mesoscopic diffusivities and thermodynamic solubilities. Applications include gas separation in nanoporous carbons and permeation through polymers.
Molecular Transport in Nanostructured Materials
NanoporousCarbon (NPC)for gas separation
TubeGen: Online Carbon Nanotube gen. program
ab initio quantum mechanical calcs. of guest-host interactions
Molecular Dynamics simulations of diffusion in polymers and NPCs
Purdue University
Seth Lloyd and David CoryMassachusetts Institute of Technology
Goals:• Use a quantum information processor (QIP)
to investigate nano and sub-nanostructures. • Explore propagation of information from the
sub-nano to macro scales.
Nanoscale Quantum Simulations
reverse map
Decoherence generates one bit of information
Density matrices
pseudo pure state
decohere bit reverse map
Implementation of the quantum baker’s map
forward map
Experimental Methods:• NMR is used as a ‘Quantum Analog Computer’ to simulate complex quantum systems in large Hilbert spaces.
• Both chaotic and regular maps can be implemented in a spin system.
Purdue UniversityMolecular Nanoelectronics: From Hamiltonians to Circuits
pseudo pure state L
MOSFET
Mark Lundstrom and Supriyo DattaPurdue University
Mark Ratner (Northwestern) and Mark Reed (Yale)
Bachtold, et al.,Science, Nov. 2001
CNTFET
Schön, et al.,Nature,413,713,2001
SAMFET
Purdue UniversityMolecular Nanoelectronics: From Hamiltonians to Circuits
Electronic DevicesClassical/quantum electronsin an open system far from
equilibrium
Chemistryquantum mechanical
electrons in isolated moleculesat equilibrium
quantum mechanical electrontransport in molecular scale
devices under bias
Nonequilibrium Green’s function (NEGF) approach with an atomic level basis
Then on to circuits and systems….
Purdue University
VD
Contact2
current
Device simulation at thenano/molecular scale
silicon dioxide
silicon dioxide
Gate
Gate
drainsourceSiO2
L = 10 nm
Xylyl Dithiol
S. Datta, et al., Phys. Rev. Lett., 79, 2530, 1997
position --->
ener
gy--
->
Purdue University
Compact models for circuits and systems
EF
EF - qVDS
Gate
Dra
in
Purdue UniversityComputational nanotechnologyis different
atomic/molecular
Gate
Gate
mesoscale devices
circuit models
Purdue University
Why compute?
• to understand
• to explore
• to design
Purdue University
Challenges inComputational Nanotechnology
• bridging length and time scales
• producing and conveying understanding
• maintaining close ties with experimentalists
• computational demands
• solving problems quickly
• collaborating and interdisciplinary research
• providing users access to simulation tools
• education and support
Purdue University
www.nanohub.purdue.edu
resource
management
Software applicationsResearch codes
PUNCH
workstationsserversLinux clusters
middleware
web enabling-network operating system-logical user accounts-virtual file system-resource management system
nanohub.purdue.edu
100 nodes (200 cpu’s)1.2 GHz / 1GB RAM
Purdue University
CNTbands
Purdue University
The nanoHUB
What can you do?
• simulate 10-nm scale MOSFETs with nanoMOS
• simulate conduction in molecules with Hückel-IV
• simulate carbon nanotube transistors with CNT_IV
• read “Resistance of a Molecule” and work exercises with Toy_Molecule
• Take a 2-day short course: “Electronic Device Simulation at the Nano/Molecular Scale”
Purdue University
The nanoHUB
Some statistics:
PUNCH: ~ 2500 users in 35 countries
>7M hits / almost 400,000 simulations
nanoHUB: 74 users in 22 countries >2000 simulations >150 source downloads
Purdue University The Network for Computational Nanotechnology
Mission To address key challenges in nanotechnology by:
1) supporting interdisciplinary research teams focused on three themes that begin at the molecular level and end at the system level.
- nanoelectronics- nanoelectro-mechanics- nano/bio
2) operate an infrastructure that supports these teams and the field of nanotechnology (computational and experimental) more generally.
Purdue University The Network for Computational Nanotechnology
Guideinfrastructuredevelopment
high-performancecomputing
visualization
nanoHUB Partners in computer science
workshopsconferences
visitorsstudents
important problems that developinfrastructure and curriculum
Supporting infrastructure
and leadership
education
open sourcesoftware
Supportsmulti-scalemulti-disciplinary research
Theme projects
Purdue University The Network for Computational Nanotechnology
Purdue University:Computing Research InstituteInformation Technology at PurdueThe Computational Electronics Group
Partners:University of Illinois, Northwestern University
Stanford, FloridaNASA Ames and Jet Propulsion Lab
Funding:National Science Foundation, ARO DURINT, Indiana 21st Century Fund, Purdue University
Purdue University
Conclusions
• Computational nanotechnology can plan a key role in realizing the promise of nanotechnology
• Rapid progress is occurring (real challenges exist)
• A Network for Computational Nanotechnology is being established to support computation and the broader nanotechnology community of researchers, educators, experimentalists, theorists, and students.
