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FCR
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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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