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Needs of Theory and Simulation forNanoarchitectonics

(FENA and WIN)

Kang L. WangKang L. WangRaytheon Professor of Physical SciencesRaytheon Professor of Physical Sciences

Center on Functional Engineered NanoArchitectonics -- FENA(www.fena.org)

Western Institute of Nanoelectronics -- WIN(www.win-nano.org)

University of California - Los AngelesLos Angeles, California 90095 –1594

(Ph: 310-825-1609 // Fax: 310-206-7154 // E-mail:wang@ee.ucla.edu)

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FENA and WIN

High Functionalityand Throughput asbenchmarked withscaled CMOS

Low powerdissipation

Variability Total solutions –

materials,technologies andother supports

system software

systemssystems

structuresstructures

materialsmaterials

physicsPhysics and chemistry

Nanoarchitecturecircuits

devices/interconnect

platform / architecture

application software

Logic Switch replacementby 2020CMOS Technology

Augmentation

15 Institutions42 Faculty

60 Students +30 postdocs

Spin Devices

Spin Circuits

Benchmark and MetricsStanford

CITRIS

NNIN

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Predicting Atomic Structure ofNanosystems

Structure/Interface

Properties

Device Performance

Today’s electronics- charge

Known precisely(i.e., diamond Si)

Can becalculated and/ormeasured

Usually not knownprecisely yet

Nano-electronicsNew state variables:e.g.,Spin, Molecule

Difficult to predictsince structuresnot known

We need methods that can reliably predictmicroscopic atomic structures of nanoelectronicdevices (equilibrium and non-equilibrium)!

Nanomaterials/ Processing

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Theory and Simulation

Predicting materials andstructure properties Alchemy Properties

Self assembly and DirectedSelf Assembly (templating) Drivers: Physical, Chemical and

Biochemical (DNA) Energetic and atomic scale Ab Initio self consistent and self

consistent field theory Device Working Principles

Physics, Chemistry Exploratory concepts Simulation/modeling

Interface

Close Collaborations among the theory and simulation talents Seamless interface Working close with Experiments

The needs: Key problems

Charge and Alternatestate variables for lowdissipation andvariability Spin Molecule/ phase transition

Device/performance Molecule devices Spintronics

Heterogeneous integration

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Modeling Across Time and LengthScales

Quantum Mechanics

Atomic Kinetics

Continuum Modeling

Need efficient, accurate and general first-principlesmethods for realistic simulations of synthesis,processing (assembly), manufacturing and operationof nanoelectronic devices and nanosystems.

Length

Time

Non-equilibriumDissipative

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Predicting Material Properties & Patterns

Left: Calculated effective inter-atomic interactions in Fe-Ag/Ru.

Simulated diffuse x-ray scatteringpattern for Fe-Ag nanowires on Ru. Simulated surface structure of Fe-

Ag/Ru, showing stripe formation.

Predictable regular patterned templates for directed self-assembly of nanostructures Be able to predict self assembled structures from ab initio (Self consistent

NEGF)VidvudsOzolins(UCLA)

GlennFredrickson

(UCSB)

• Example of a numerical SCFT (self-consistentfield theory ) simulation of 8 unit cells of theIa3d “gyroid” phase of diblock copolymers.

• This project aims to develop a similar high-resolution SCFT for thin block copolymerfilms relevant to nanoscale lithography

Simulation of block copolymer assemblyfor nanoscale lithography

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Rotaxane: Mechanically-Interlocked Moleculefor Next Generations of nanosystems

• Develop rotaxane-based memory device• Device characteristics: Ultra-high Density

and Stable Response

Prof. William A. Goddard, and Seung Soon JangGraduate student: Hyungjun KimCollaboration with Prof. J. Fraser StoddartRing Location (x)

Ener

gy (e

V)

OFFON

-e–

OFF

ON

0

ΔG‡

IonizationPotential

ΔG

+e–

Molecular conformation as a state variable

• 160 Kbit memory fabricated and tested @ 1011

bits/cm2 +- 2 operation.

• Crossbar: 400 Si nanowire bottom electrodesand 400 crossing Ti nanowire top electrodes(wires: 16 nm diameter / 16.5 nm half pitch)

1 2 2~ exp ( )

store b

tun

ma E

P!

" #= $ %$ %

& 'h

mLtsw~

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First-Principles Interface Engineering

Nanoelectronics will includemetal & semiconductornanostructures, organic (andbio) molecules.

Importance of interfacesincreases with decreasingfeature size.

Need first-principles methods to:• Understand, predict and optimize the structure and

thermodynamic stability of interfaces in nanosystems• Predict charge and spin transport across nano-

interfaces

Pentanethiol SAM on Au(111)

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Rotaxane

TTF (OFF) DNP (ON)Positively chargedmonopyridine

-6

-4

-2

0

2

4

6

8

10

0 10 20 30 40 50

Coordinate (Angstrom)

Me

an

Fo

rce

(kcal/m

ol A

)

0

10

20

30

40

50

60

70

0 10 20 30 40 50

Coordinate (Angstrom)

Po

ten

tia

l o

f M

ea

n F

orc

e

(kc

al/

mo

l)

Free Energy Barrier ~ 60 kcal/mol

Rotaxane: Mechanically-Interlocked MoleculeCurrent work

( ) ( )( )R

rxn

rxn rxn

dF RF R F dR

dR!

"# $"= ! % %& '"( )

*Mean Force

Potential of Mean Force Approach

How to control the free energy barrier between the ON state and the OFF state

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Self assembled nanoarchitectonics and theirheterogeneous integration on Si

Approach: Directed self-assembly using PNA- I-junction dsPNA, T-junction dsPNA

Electrical characterization of I- and T- junction

SWNT

PNA

100 nm

100 nm

SWNT

SWNT

PNA

Mihri Ozkan(UCR)

CNT – Molecular RTDRequires accurate modelingof Structure Excited states Non-equilibrium

potential Electron / phonon

interaction Vibrational modes Phonon (thermal)

transport

Roger Lake(UCR)

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Simulations of Self-Synthesized FunctionalDevices.

Bio-assembled CNTFETs – DNA and PNAassembly First simulations of the CNT-ssDNA-CNT system.

Experimentally measured I-Vof CNT-ssDNA-CNT

FIREBALL / NEGF calculations oftransmission and spectral functions at

transmission peaks a and b.

CNTFET drain current vs.gate voltage for different

lead doping

Non-equilibrium Green’s function Dissipation

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Example of Interface Geometry Effect

CNTs connected by conjugated molecules

Left: CNT-(CH)20-CNT with the polyacetylene co-planar with the tangential plane of the CNT (topstructure) and perpendicular to the tangentialplane of the CNT (bottom structure) at the pointof contact.

Transmission for the co-planar geometry is, on average, 3-ordersof magnitude larger than transmission for the perpendiculargeometry.

Electron transfer is a strong function of the interface geometry.

Relaxed

E.G. X. Guo et al., “Covalently Bridging Gaps in Single-Walled Carbon Nanotubeswith Conducting Molecules,” Science 311, 356 (2006).

Right: Transmission versus energy plotfor both structures. The CNTs aremetallic (12,0).

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Simulation and Computation of NovelEngineered Nanomaterials and Devices

Phonon engineering: enhance electrontransport in nanoscale transistor channels andimprovement of heat removal and thermalmanagement to guide device design Alex Balandin

(UCR)

• The results (Nano Letters,2006) overturn conventionalbelieve that the phononconfinement effects arealways detrimental to thecarrier mobility.

• Carrier mobility in Sinanowires can be greatlyenhanced by embedding thenanowires within hardmaterials such as diamond.

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Ultra-scaled device modeling needs

3D quantum mechanical electrostatics /band structure and physical transportmodels for devices-including strain, high-k/metal gate, UTB, and surface orientationeffects

Physical models for transport in beyond-Sidevices (Ge, III-V) enabling performanceprediction/analysis

Efficient simulation of dissipative QMtransport, especially using a comprehensiveset of scattering mechanisms

H.H. Hosack, Frontiers in Comp. Nanoelect., 2/20/07, Indianapolis,https://www.nanohub.org/resources/2380/

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Theory and Simulation – WIN(Spintronics) Spin materials

DMS Multiferroic materials and devices Room temperature materials

Electric field control devices – Rashba (spin orbit interaction) Active control of dynamics: e.g., Spin torque

Spin Hall effects Theoretical foundation

Spin and Magnetic Devices – Empirical approach in simulation Switching mechanisms: Spin transport Need to have fundamental approach: collective phenomena

Device models for circuits

Self consistent – NEGF Non-equilibrium quantum mechanics Theory of damping, dissipation Many body effects

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Atomic-Level Materials Design

Theory can suggest: Which specific material or ordered structure has desired

properties How to grow such a structure experimentally

Example: Raising the Curie temperature of a magnetic semiconductor(Ga,Mn)As for spintronics applications

A. Franceschetti, S.V. Dudiy, S.V. Barabash, et al., Phys. Rev. Lett. 97, 047202 (2006).

Unoptimized Ga0.75Mn0.25As(random alloy)

Optimized Ga0.75Mn0.25As:(201) superlattice

Tc~240K — too low Tc~360K — sufficient to use

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Atomic-Level Materials

For spintronics, atomic-level optimization couldtarget:

Raising ferromagnetic transition temperature Tc Ensuring magnetic semiconductors indeed half-metals

(avoiding structures that mix spin channels in magnetic state) Adjusting doping-dependent profiles Increasing barriers for unwanted defects Impurity

Example: half-metallicity in magnetic (Ga,Mn)As

• Calculation predicts that in perfectly randomGa1-xMnxAs,both spin-up and spin-down densities of states(DOS) become non-zero at εF by x=0.125.

• Can atomic-level optimization bring back half-metallic properties that are vital for spintronicsapplications?

Figure adopted from:E.Kulatov, H.Nakayama et al., Phys. Rev. B 66, 045203 (2002).

Ga0.875Mn0.125As

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Theoretical Work and NumericalModeling:

Empircal Logic device functionality using spin

wave superposition Nano architectures with spin wave bus

Gated Spin Wave Devices & Bus –A. Khitun and K Wang

Spin wave propagationestablished

Spin wave resonancefrequency occurring atf ~ H1/2

0( 4 )H H M! " #= +

0 50 100 150 200 250 300

External magnetic field (Oe)

Amplitude changes (dB)

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

Fre

quency

(G

Hz)

-4dB

-3dB

-2dB

-1dB

0dB

1dB

2dB

3dB

4dB

Alex Khitun(UCLA)

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Spin Transistors -- Spin current amplifierJoachim Stöhr

Achievement: Direct observation of spin transfer switching by x-ray microscopy.

Joachim Stöhr – SLACwith Yves Acremann

d) 8.6 ns e) 9.0 ns f) 9.6 ns

g) 12.0 ns h) 12.2ns

i) 13.2 ns

a) 0 ns b) 0.15ns

c) 0.6ns

a

b

c

def

ih

gb

c

de fg hh

i

Y. Acremann et al., PRL 96, 217202/1-4 (2006)

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Spin Device Modeling

Spin Hall Field EffectTransistor: does not requireelectron transport and hencecan potentially be an lowdissipating device

Quantum Spin Hall Helicaledge states

Support and GuideExperiments

Quantum Spin Hall Field Effect Transistor

Science, 314 1757 (2006)

B (T) -0.06 0 0.06

θ K (µ

rad)

0

4

-4T = 20 K 3 mV/µm

y = -48 µm

y = +48 µm

xy

kjs

B E

David Awschalom

Quantum phase transitionSpin Orbit interaction

SC Zhang(Stanford)

T = 295 K-0.05 0.0 0.05

B (T)

θ K (µ

rad)

-0.3

0.3

0.0

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In A Nutshell

Nanomaterials Assembly and nanopatterning

Alternate state variables Spin variables: electron, nuclear spin, spin

waves Molecular state, Phase transition, Dipole,

Phase, Spin FET, Spin torque, Spin wave packets

propagation

Devices New principles Non-equilibrium

Hetergeneous Nanosystems Integrated Efforts

Theoretical approach to come to closeexperimental

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Acknowledgments

All the FENA and WIN participants All students, postdoctoral fellows and

visitors as well as collaborators aroundthe world

Support: SRC, NSF, Marco, NERC, ARO, AFOSR,ONR, DARPA and many industrial companies

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Contact Info

Kang Wang (Director): wang@fena.org;wang@win-nano.org

Kos Galatsis (COO): kos@fena.org,kos@win-nano.org

Admin: fena@fena.org, win@win-nano.org

FENA Center and WINRoom 5289 Boelter Hall

University of California, Los AngelesLos Angeles, CA 90095-1594