The NCN and Modeling and Simulation of Nanosystems

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Network for Computational Nanotechnology (NCN) Purdue, Norfolk State, Northwestern, MIT, Molecular Foundry, UC Berkeley, Univ. of Illinois, UTEP

Gerhard Klimeck Director Network for Computational Nanotechnology (NCN) Purdue University [email protected]

2010 NSF Nanoscale Science and Engineering Grantees Conference, Gerhard Klimeck Dec. 6th, 2010

What is NCN?

Infrastructure and Research Network for computational nanotechnology Operates nanoHUB.org Conducts research, uses nanoHUB.org

Gerhard Klimeck 2

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1965 Gordon Moore http://www.intel.com/technology/mooreslaw Relative Manufacturing Cost per Component Number of Components per Integrated Circuit

Intel in 2009

Device Size: Tens of nanometers Stanford SUPREM RobertChau (Intel), 2004

http://www.intel.com/technology/mooreslaw/index.htm Device Integration: >2 Billion Berkeley SPICE

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Berkeley

Simulation Program with Integrated Circuit Emphasis.

from: Larry Nagel, BCTM ‘96 •Started as a class project •Developed as a teaching tool

Ronald •Quality control: pass Pederson A. Rohrer http://www.omega-enterprises.net/ •Dissemination: Laurence W. Nagel Public domain code Pederson carried tapes along Donald O. Pederson Students took it along to industry and academia

Released 1972 5

Stanford

Stanford University PRocEss Modeling

•Stanford wanted to mimic Berkeley success •Combine various existing models •Dissemination: Public domain code Community workshops Students took it along to industry and academia

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Birth of an Industy

Process Simulation Device Size Intel Capitalization: $85B Total Industry: $280B Circuit Transistors Transistors Simulation

Years 7

What’s Next? nano-scale Nano Initiatives structures Device Size

Electronics Materials Research Photonics Mechanics Bio/Medicine Transistors Transistors Billionsstructures of nano

Years

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NEMO 3-D Technical Approach

Approach: Use local orbital description for individual atoms in arbitrary crystal / bonding conguration Use s, p, and d orbitals. Use genetic algorithm to determine material parameter tting Compute mechanical strain in the system. Develop efcient parallel algorithms to generate eigenvalues/vectors of very large Demonstration / Capability / Impact: matrices 52 million atom electronic structure (101nm)3. Quantum dots, nanowires, quantum computing Gerhard Klimeck 9

NEMO 3-D Multi-Scale Modeling

Atom Geometry Positions Construction ~10-50 million o Valence Force Field Atomistic atoms Strain (VFF) Method Relaxation

Piezoelectric Hamiltonian Electrical / o Piezoelectric eff. Valence Potential Construction Magnetic field Pol. charge density Electrons o Empirical tight binding Single Particle Optical Prop. ~0.5-10 million 3 5 States Electronic Str sp d s* + spin orbit

e-e Many Body Optical Prop. o SCP: Poisson + LDA interactions Interactions Electronic Str Few States o Slater Determinants

Gerhard Klimeck

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NEMO 3-D Multi-Scale Modeling InGaAs: bi-modal bonds InAs

Atom Geometry Positions Construction ~10-50 million o Valence Force Field Atomistic atoms Strain (VFF)GaAs Method Relaxation

e-e Many Body Optical Prop. o SCP: Poisson + LDA interactions Interactions Electronic Str Few States o Slater Determinants

Gerhard Klimeck

How do we get QDs? Stranski-Krastanow Growth

Self-Assembly Process InAs deposition on GaAs substrate

InAs (0.60583 nm) First Layer (wetting layer) ~ 1ML

GaAs (0.56532 nm) GaAs

InAs

GaAs GerhardGaAs Klimeck 12

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Capping with Intermediate Alloy

GaAs GaAs

InAs InAs

GerhardGaAs Klimeck GaAs 13

Quantitative Quantum Dot Modeling Optical TCAD - Optical Wavelength Tuning Objective: Optical emission at 1.59m without GaN Understand experimental data on QD spectra in selective overgrowth 17 experimental data points growths Tatebayashi, et al, Appl. Phys. Lett., V78. 3469. Approach: Model large structure 60nm x 60nm x 60nm 9 million atoms No changes to the previously published TB & VFF parameters Result: Theory (red line) matches a sequence of 17 experiments (black dots/lines) Bi-modal In-As,Ga-As bond distrib. change in quantum dot aspect ratio Quantitative model of complex system IEEE T Nanotech, Vol. 8, pp. 330 (2009) Gerhard Klimeck

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Overcoming barriers to quantum mechanics simulations in physics, chemistry, biology, and materials to migrate nano-science to nano-technology.

Algorithms, Computing, Open, available, Middleware, Service usable by real users

NCN / nanoHUB.org Develop & Deploy Methods, Tools, and Training atomistic mesoscopic systems

Impact on Research Impact on Education

Example: NEMO3D – Nanoelectronic Modeling

Algorithms, Computing, Open, available, Middleware, Service usable 2,600 usersby worldwide real users

NCN / nanoHUB.org Develop & Deploy Methods, Tools, and Training atomistic mesoscopic systems

Towards Peta-Apps Impact on Research• 52M atoms on Impact on Education nanoHUB H/W NEMO 3-D at 7 tutorials • 8,192 cores IBM B/G and Cray XT3/4 SC05, SC06. SC07, TG06, TG7, Nano06, NMDC06, NEMS07, >130 educators

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Over 180 tools online!

Over 2,300 Resources!

180 tools 43 courses

1,700 seminars and teaching materials

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It Happens Here

Nano App Store

165,000 users worldwide As much traffic as www.purdue.edu Users at all Top 50 US Engr Schools 19% of all .edu domains

172 countries

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nanoHUB on iTunes U

Nov 2009 start

350 content items today

55,000 downloads

~10,000 downloads/month

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Wikipedia Contributions

Punjabi German Italian

16 animations deployed Jan 2010 on ~30 pages Brings 2,200 visitors for 4,000 visits monthly

Sociology How do Users Behave? • Questions: – How many students in the class? – Which tools? – Intensity of use – Sustained use

– Percentage of service: Education vs. Research use • Some Statistics – 8,600 users ran 345,000 simulations last 12 month – 116 classes / 97 institutions in 09/10 • Info Obtained from self-registration, manual follow-up – 575 citations in the literature • Info obtained from Google Scholar and manual analysis

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Sociology How do Users Behave? • Questions: – How many students in the class? – Which tools? – Intensity of use – Sustained use – Percentage of service: Education vs. Research use • Can we get answers? Automatically? => Improve Services – Better tool classification – Customize web page for users, customize learning experience – Computational resource provisioning •Broaden user base

The Matrix

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The nanoHUB Matrix Users

Time (days)

Each dot represents simulation activity on a particular day The color of the dot indicates a particular tool

We will look backwards into history for each user in the past 12 months And plot ALL their activities

The nanoHUB Matrix Formal Education vs. Research

single tool, single use / homework

single tool, sustained, intense

multiple tools, sustained, periodic Proof of use in EDUCATION! Users multiple tools, Knowledge Transfer out of Researchsmall class 120 institutions, 95% outside NCNresearch, Voluntary / Viral Use self-study Time (days)

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Proof of use in RESEARCH!

Over 1,200 authors, 77% non NCN Proof of voluntary use by OTHERS

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30 30

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Research: Publish or Perish

Academy of Engineering Member

575 nanoHUB citations >3,200 secondary citations Faculty member h-index: 27 3 years after PhD

New Incentives! The Next Generations nano-scale structures Device Size Tools Researchers Publications Transistors Transistors Billionsstructures of nano

Years

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NCN Simulation-Driven Research

OMEN Seeded 2006 Intel supported Peta-Apps Award in 2007

13 journal, 11 proceedings

Runs on fastest computer in the world

Runs 5 tools in nanoHUB >3,500 users

Next Generation Faculty:

Usage at SIUC

Shaikh Ahmed Post Doc Faculty at 6,183 users at Purdue SIUC 8 tools • Infused nanoHUB into Get images from annual report existing classes • Built a new nanoelectronics curriculum • Used nanoHUB for research

Recently Dr. Ahmed was promoted to tenured Associate Professor. I would like to emphasize that Dr. Ahmed's use of nanoHUB in education and research, which earned him national and international visibility, did play a significant positive role in his early promotion case. Glafkos Galanos Chair, Dept. of Electr. and Comp. Eng, SIUC

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Next Generation Publications Research Incentives Tool Usage reading papers

Dragica Vasileska 17 tools 7,835 users 115 citations

The World’s Largest Nano Userfacility

165,000 users worldwide As much traffic as www.purdue.edu Users at all Top 50 US Engr Schools 19% of all .edu domains

172 countries

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Typical Dissemination Paths

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Problems: ' • REALLY LONG stove pipe " •Web content: afterthought usually stale •Data shared by email ( " •Tools spread by hiring $ % nanoHUB Technical Solution

" " "

" $ # "

Problems: ' • REALLY LONG stove pipe " •Web content: afterthought usually stale •Data shared by email ( " •Tools spread by hiring $ %