center for computational nanoscience - asuthe center for computational nanoscience ... enables large...

The Center for Computational Nanoscience Mark van Schilfgaarde, Director

•

A formal vehicle to forge links in research initiatives– Computational initiatives can be stronger when they can draw on a center– Facilitates finding areas of common interest, and links between ASU with

outside interested in collaborative research or supporting research.

•

A facility to invite visitors– Hope to draw leading experts as colloquium speakers– Possibly visitors can come for short states and collaborations

•

Better communication among existing faculty– Internal seminars inform the rest about what interesting is going on at ASU– Integrate Education component in computational techniques

•

Coordination with other centers, e.g. HPCI, Purdue Nanohub, IGERT, MRSEC

High-Performance Computing Initiative

ASU is fortunate to have an excellent center for high performance computing, initially sponsored by Ira Fulton.Prof. Stanzione

(director) has expanded the initial

center into a 1000 processor facility.Nodes are free

up to a certain maximum, after which

HPC charges $0.25/CPU hour ---

enables large scale calculations at minimal cost.HPC supplies software support of standard computer and scientific packages

(free) and custom support.

HPC offers classes on advanced programming techniques, e.g. code parallelization.Enables faculty to extend range of research, and frees them from maintaining their own facilities.

Classification of Expertise by length scaleASU’s

expertise in theory and computation spans many

disciplines,

ranging from atomic to the macroscopic scale:

First principles, many-electron Schrodinger equation applied to both ground state

properties (total energy,

phonons, etc) and excited state

properties (transport)

Semiclassical particle models for

electron transport

Model forms of the SE (electrons; nucleii

“classical”)

Increasing length scale

Classical atomistic models for: structure and transport

Constitutive relations and many other (>nano)

Representative Faculty (many departments)Ab

initio SE, ground state: Adams (thermochemistry),

Chizmeshya (carbon sequestration), Friesen (later), Rez (oxides), Seo (oxides)

Model SE , excited state: Ferry, Goodnick, Saraniti, Vasileska (later), Shumway (Quantum Monte Carlo, mostly dots)

Ab

initio SE, excited state: : Kotani (later), Sankey (molecular and biological systems), van Schilfgaarde (Magnetism, GW)

Classical atomistic methods: van der Vaart (later), Saraniti (later), Thorpe (rigid body dynamics of nano-bio molecules)

Semiclassical

electron transport: Ferry, Goodnick, Saraniti, Vasileska (later),

Grand Challenges for Computation:

1.

Extend reliability of theory: At the most fundamental level, this is solving the SE to high accuracy. (Example GW theory, Kotani). As length scales increase approximations must, also. A significant challenge at any level.

2.

Find alternatives that implement existing theories in more efficient ways.

3.

Develop techniques that bridge length scales.

Many ways to develop capacity to study new phenomena, or explain phenomena better. Three main classes:

–

Source: Physics Today 58, 49 (2005)

•

The 3 most cited

papers, and 6 of the 11 most cited papers in the Physical Review

series (Phys. Rev. B, Phys. Rev. Lett., Rev. Mod. Physics) all have to do with ab initio approaches to solving the fundamental (Schrodinger) equation

for the electrons.

•

Walter Kohn won the Nobel Prize

for Density Functional theory in 1998.•

Today, it is a major tool

in almost every branch of science and engineering.•

Applicable to whole periodic table; however, its reliability is imperfect, and it is too expensive

for even the mesoscale.

Success story: QSGWQuasiparticle

Self-Consistent GW (QSGW)

is the most accurate

and universally applicable ab initio theory yet developed for solids.

Example: semiconductor bandgaps

predicted to

~0.2 eV

of expt.

LDA

Example: Third-generation Photovoltaics•Growing acceptance that the U.S. must reduce its reliance on oil.–Photovoltaics

must be critical component of this, because their high conversion efficiencies.

–High conversion efficiencies

and low cost

are key barriers to practical realization today.

–A collection of new ideas

have been proposed to increase conversion efficiencies and surmount the standard Schockley-Quiesser

limit. They are called “third-generation”

technologies. They include:

•“selective energy”

contacts that collect electrons before thermalizing•Multiple e−h

generation / incident photon through impact ionization•Multiple e−h

generation / incident photon through impurity bandsBasic problem: none of these ideas has been realized in practice!Are they even feasible?

•

Are 3rd

generation proposals workable?A hard question

to answer experimentally.Theoretical treatment so far has been too

simpleGood modeling should: predict “best case”

scenarios, and the effect of nonidealities

that can occur under realistic conditions.

“Cellular Monte Carlo” simulator based on the Boltzmann equation

(Saraniti

and Goodnick). Models realistic devices

with realistic parameters. The most sophisticated tool of its kind, it can potentially give a reasonable answer to those questions, before spending large resources on trying them.

Example: GaAs MESFET

Bridging length scalesThe CMC

solves the Boltzmann equation

for realistic devices. But it requires as input parameters such as energy bands

and scattering matrix elements. If those inputs are good, the calculation should reliably predict

device behavior. Ab initio theory such as QSGW can supply accurate inputs.

Example: MESFET in GaAs. Much experimental data; reliable model to generate inputs (EPM) can be developed from this data.

Alternatively, use QSGW. Doesn’t rely on experiments! In this case, results almost identical. What about InN?

Ab

initio Electrochemistry (Friesen)

•

A key loss mechanism

in catalysts: The voltage required to generate a practically acceptable current (overpotential) is higher than the thermodynamic potential.

•

This difference occurs through losses

on the atomic scale as the ions cross the electrode/electrolyte

interface.

•

Details are poorly understood, particularly in protic

ionic liquids (little-studied)

•

Plan: use ab initio framework to model the electrodode, electrolyte, and interface; and the ion exchange mechanism.

•

Challenge: interfaces are too big

to realistically attack with good quality present-day modeling methods

based on the

local-density approximation to density-functional theory•

Project includes development of next-generation methods

that will make this possible.

Multi-modal, Bi-functional Electro-Catalysts

Renewable Energy Electrochemistry

Tuning Activity

-Tune hybridization w/ Adsorbates-strain-eigenstrains

Multi modal catalysts provide:-Enhanced transport kinetics-High surface area

EF

Strain

Reactivity

Charge transfer

Reactivity

d-bandDepth Strain

Constitutive Relation

CatalyticActivity

ConstitutiveRelation Constitu

tive

Relation

EF

Strain

Reactivity

Charge transfer

Reactivity

EFEF

Strain

Reactivity

Charge transfer

Reactivity

d-bandDepth Strain

Constitutive Relation

CatalyticActivity

ConstitutiveRelation Constitu

tive

Relation

Friesen Research GroupSchool of Materials, Arizona State University

Tuning catalytic activityTune activity by applying Physical strain: Max ~1%

Tune sp-d hybridization w/ physical strain.

-3 -2 -1 0 1 2 3

-4-202468

10

f_fin

ite (N

/m)

Strain (%)

Intrinsic f

-EigenstrainStrong adsorber

-3 -2 -1 0 1 2 3

-4-202468

10

f_fin

ite (N

/m)

Strain (%)

Intrinsic f

-EigenstrainStrong adsorber

Tune activity by modifying Eigenstrain:>4% achievable through adsorption

Constitutive relations exist between surface stress, strain, eigenstrain, and d-band position.

Map d-band position as a function of surface stress change w/ adsorption to tune in real- time.

Electronic structure calculations allow for the direct analysis of surface stress (~eigenstrain) and d-band position changes in the presence of adsorbed species. Experiment can then directly monitor catalytic activity and surface stress changes in real-time.

Molecular Dynamics of Folding/Unfolding/RefoldingConformational Changes

Conformational change = The change in shape ofa biomolecule

upon

binding other molecules

Arjan

van der

VaartCenter for Biological Physics

Department of Chemistry and Biochemistry

1. transport proteinse.g. maltose-binding protein

3.

molecular motorse.g. FO

F1

-ATPase

2. kinasestransfer phosphate groups from high-energy donor molecules (e.g. ATP), to specific target molecules. Very common in the body.

4. etc. etc.

Conformational changes are crucial

for the functioning of many proteins

Src tyrosine kinase

1. What interactions lead to the conformational behavior?

2. What are the pathways for the conformational change?

3. What is its biological function?

4. Can we block the conformational behavior?

1. High spatial resolution

2. All interactions are quantified

3. Thermodynamic properties can be calculated

Method:

Atomistic Molecular Dynamics Simulations

How do conformational changes work?

Atoms treated as classical point-masses moving in a model potential

– +

Bonds Angles DihedralsImproperDihedrals Electrostatics

van derWaals

Bonded terms Non-bonded terms

kb

(r–r0

)2 ka

(θ–θ0

)2 (q1

q2

)/(εr) E[(rm

/r)12–(rm

/r)6]

The model potential is fitted to experiments

and quantum mechanical

calculations

The potential must be transferable

Atomic motion propagated by classical (Newtonian) dynamics using a very small timestep

(10–15

seconds)

ki

(ω–ω0

)2kd

[1+cos(nφ–φ0

)]

Molecular Dynamics (MD)

protein domain motionunwinding of DNA helix

protein folding

molecular dynamics small systemmolecular dynamics large system

10-6 10-3 1 103 s10-15 10-12 10-9

Thus: need to develop “smart”

techniques!

Problem: Timescales of conformational motion

1

Takao Kotani，Mark

van Schilfgaarde

Quasiparticle

Self-consistentGW (QSGW) method

2

Our questions are

• What defects reduce the performance of HfO2 ? • Spin-polarized current for MRAM, Fe/MgO/Fe• Interpret EELS data to guess atomic structure• New “thermo-power material”?--- We have to know electronic structure ---

So many first-principle calculations are performed now.

3

Difficulties in first-principle methods.

(1) Computational costs(2)

We can not evaluate quantities

directly. E.g. Tc

for High-Tc

SC.(3)

Poorness of Standard approximations,

LDA (local density approx.) or soour QSGW method can solve this problem.

(3) is serious ---“reliability”

can be still problematic.

4

Poorness of standard approximations

Experimental band gap (eV)

CoO

is insulator (gap ~4 eV); LDA predicts metallicLDA+U works. But what U?

5

Quasiparticle self-consistent GW method

Optimum independent-particle picture. Thus, ready to be used for device simulators.

It looks like Hartree-Fock method, but it uses “Dynamical Screened Coulomb interaction *” instead of the bare Coulomb interaction .

*it is determined self-consistently.

6

Our Result by QSGW

7

Experimental band gapvs.

Calculated band gap

QSGW: Good!Almost along the diagonal line!

LDA, GW

Experiment

8

GaAs

QSGW: greenO: Experiment

m* (QSGW) = 0.073m* (LDA) = 0.022m* (expt) = 0.067

Ga d level well described

Typical case for semiconductor

9

ZnO

dielectric

0 10 20 300

1

2

3

4

5

ZnO

with LFCwithout LFCexpt.

Im ε

ω(eV)

10

0

20

40

60

80

100

Γ MX[ζ,ζ,ζ]

meV

[ζ,−ζ,−ζ]

QSGWLDAExperiment

Spinwave dispersion (MnO AF-II)

*Energy bands, and optical properties arealso described well.

11

•

The QSGW

method Self-consistent

perturbation theory

-

optimum independent-particle picture.

-

wide range of materials.

-

no parameters like LDA+U.

QSGW is the very basis for next-generation electronic structure calculation.

-----------------* How to make speed up?* Phonon should be included.* Make it more accurate.

Conclusions

"

TYR40

ARG42

ARG82

ARG132

TYR102

TYR106

ASP113

GLU117

Modeling and Design of Modeling and Design of BiomimeticBiomimetic NanoconductorsNanoconductorsMarco Saraniti, Sasha Smolyanitsky, and Prathibha Ramaprasad

Surface Charge Density on SiOSurface Charge Density on SiO22 PoresPores

bulk pHsu

rface

char

gede

nsity

[C/m

2 ]4 5 6 7 8 9

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

model, 0.2M KClmodel, 0.067M KCl

experiment, 0.2M KCl 1

experiment, 0.067M KCl 1

1. Patricia M. Dove and Colin M. Craven. Surface charge density on silica in alkali and alkaline earth cloride electrolyte solutions. Geochimica et Cosmochimica Acta, 69(21), 2005.

Conductance (pH=7)Conductance (pH=7)

KCl buffer concentration [M]

cond

ucta

nce

[nS

]

10-6 10-5 10-4 10-3 10-2 10-1 100

10-2

10-1

100

101

102

32 nm pore diameter64 nm pore diameter128 nm pore diameter

BD, 32 nm pore diameterBD, 64 nm pore diameterBD, 128 nm pore diameter

3D Brownian Dynamics Simulation3D Brownian Dynamics Simulation

x [nm]0

1020

y [nm]

010

20

z[n

m]

0

10

20

30

40

500.80.750.70.650.60.550.50.450.40.350.30.250.20.150.10.05

concentration, M

cations

x [nm]0

1020

y [nm]

010

20

z[n

m]

0

10

20

30

40

500.50.470.440.410.380.350.320.290.260.230.20.170.140.110.080.05

anions

x [nm]

z[n

m]

5 10 15 20

5

10

15

20

25

30

35

40

45

500.0880.0750.0630.0500.0380.0250.0130.000

potential [V] (slice at y = 10.5 nm)

pore bias [V]

pore

curr

ent[

nA]

-0.4 -0.2 0 0.2 0.4-10

-8

-6

-4

-2

0

2

4

6

8

10

0.6M buffer KCl

0.2M buffer KCl0.4M buffer KCl

pHpH--Dependent ConductanceDependent Conductance

Computational ElectronicsComputational Electronics

DragicaDragica VasileskaVasileskaProfessorProfessor

Arizona State UniversityArizona State University

PrePre--1990 Simulation Efforts1990 Simulation Efforts

SemiclassicalSemiclassical::Monte CarloMonte Carlo--Molecular Dynamics Simulations (FerryMolecular Dynamics Simulations (Ferry--LugliLugli))

Quantum: Quantum: Wigner Function Simulations (Wigner Function Simulations (KluksdahlKluksdahl, , RinghoferRinghofer, Ferry), Ferry)Quantum Hydrodynamic simulations without and with inclusion of Quantum Hydrodynamic simulations without and with inclusion of discrete impurity effects (JR Zhou)discrete impurity effects (JR Zhou)

PostPost--1990 Simulation Efforts1990 Simulation Efforts

SemiclassicalSemiclassical3D Drift3D Drift--diffusion simulations for discrete impurity effects diffusion simulations for discrete impurity effects examination in 50 nm MOSFET devices (Vasileskaexamination in 50 nm MOSFET devices (Vasileska--1996)1996)First 3D Monte Carlo device simulation code with a real First 3D Monte Carlo device simulation code with a real space treatment of the short range electronspace treatment of the short range electron--electron and electron and electronelectron--impurity interactions (Gross, impurity interactions (Gross, VasileskaVasileska, Ferry, Ferry--1997)1997)Development of a variety of 3D Efficient Poisson Equation Development of a variety of 3D Efficient Poisson Equation Solvers (Solvers (WiggerWigger, Speyer, , Speyer, VasileskaVasileska, , GoodnickGoodnick, Saraniti, Saraniti--1997)1997)Development of a fullDevelopment of a full--band CA Monte Carlo device band CA Monte Carlo device simulation code (simulation code (SaranitiSaraniti, , WiggerWigger, Goodnick, Goodnick--1997)1997)Inclusion of the effective potential in Inclusion of the effective potential in semiclassicalsemiclassical Monte Carlo Monte Carlo device simulation schemes (device simulation schemes (VasileskaVasileska, Ferry, Ringhofer, Ferry, Ringhofer--2000)2000)Development of the first 2D thermal device simulation Development of the first 2D thermal device simulation code (code (RalevaRaleva, , VasileskaVasileska, Goodnick, Goodnick--2007)2007)

MOSFETs - Role of the E-E and E-I

0

100

200

300

400

100 110 120 130 140 150 160 170 180

with e-e and e-imesh force only

Ele

ctro

n en

ergy

[m

eV]

Length [nm]

VD=1 V, V

G=1 V

channel drain

0

100

200

300

400

500

600

700

800

0.12 0.13 0.14 0.15 0.16 0.17 0.18

Ene

rgy

[meV

]

Length [nm]

0

100

200

300

400

500

600

700

800

0.12 0.13 0.14 0.15 0.16 0.17 0.18

Length [nm]

Ene

rgy

[meV

]

mesh forceonly

with e-e and e-i

Individual electron trajectories over

time

-1x107

-5x106

0

5x106

1x107

1.5x107

2x107

2.5x107

0 40 80 120 160


Drif

t vel

ocity

[cm

/s]

Length [nm]

VD=V

G=1.0 V

source drainchannel

-1x107

-5x106

0

5x106

1x107

1.5x107

2x107

2.5x107

0 40 80 120 160


Drif

t vel

ocity

[cm

/s]

Length [nm]

VD=V

G=1.0 V

source drainchannel

MOSFETs - Discrete Impurity Effects

0

20

40

60

80

100

0 1 2 3 4 5

σVth

=> approach 1σ

Vth => approach 2

σVth

=> our simulation results

σ Vth [

mV

]

Oxide thickness Tox

[nm]

05

10152025303540

1x1018 3x1018 5x1018 7x1018

σVth

=> approach 1σ

Vth => approach 2

σVth


σ Vth

[m

V]

Doping density NA [cm-3]

10

20

30

40

50

60

20 40 60 80 100 120 140

σVth

=> approach 1σ

Vth => approach 2

σVth


σ Vth

[mV

]Device width [nm]

effeff

A

oxox

ABSiBBSi

Vth WLNT

Nqq/Tkq 44 3

434

⎥⎦

⎤⎢⎣

⎡ε

+φε

φε≈σApproach 2 [2]:

⎟⎟⎠

⎞⎜⎜⎝

⎛=φ

εφε

=σiAB

Beffeff

A

oxoxBSi

Vth nN

lnqTk

;WL

NTq

44 3

2Approach 1 [1]:

[1] T. Mizuno, J. Okamura, and A. Toriumi, IEEE Trans. Electron Dev. 41, 2216 (1994).

[2] P. A. Stolk, F. P. Widdershoven, and D. B. Klaassen, IEEE Trans. Electron Dev. 45, 1960 (1998).

Lattice Heating and Scaling of Transistor Dimensions

25 nm FD SOI nMOSFET (Vgs=Vds=1.2V)

10 20 30 40 50 60 70

3

8


20 40 60 80 100 120

3

12


20 40 60 80 100 120 140 160 180

3

15

2727

300

400

500

300

400

500

300

400

500

source contact drain contact

source region drain region


50 100 150 200

3

20


50 100 150 200 250

3

21


50 100 150 200 250 300

3

23

4343

300

400

500

600

300

400

500

300

400

500

Conventional SOI Transistors vs. SOD transistors with different boundary con-dition for the temperature on the gate

0 25 50 7575

0

10

20

30

40

50

60

x (nm)

y (n

m)

300

350

400

450

500source drain

BOX

0 25 50 7575

0

10

20

30

40

50

60

x (nm)

y (n

m)

300

350

400

450

500source drain

BOX

0 25 50 7575

0

2

4

6

8

10

x (nm)

y (n

m)

300

305

310

315

320

325

330

335

source drain

0 25 50 7575

0

2

4

6

8

10

x (nm)

y (n

m)

300

320

340

360

380

400

source drain

0 25 50 7575

0

2

4

6

8

10

x (nm)

y (n

m)

300

310

320

330

340

source drain

0 25 50 7575

0

2

4

6

8

10

x (nm)

y (n

m)

300

320

340

360

380

400

source drain

SOI Device

SOD Device

SOAlN Device

Notice better heat spread in the SOD and the SOAlNdevices.

QuantumQuantumGreenGreen’’s function s function codelcodel for the lowfor the low--field mobility calculation field mobility calculation (1993(1993--Vasileska, Ferry) Vasileska, Ferry) –– For the first time we have For the first time we have included selfincluded self--consistent Born approximation in realistic consistent Born approximation in realistic transport calculationtransport calculationDevelopment of SCHRED (VasileskaDevelopment of SCHRED (Vasileska--1997)1997)Development of a variety of 2D/3D Schrodinger Poisson solvers Development of a variety of 2D/3D Schrodinger Poisson solvers for simulations of quantum dots in Si and for simulations of quantum dots in Si and GaAsGaAs technology technology ((VasileskaVasileska, Ahmed , Ahmed -- 1999)1999)Effective potential inclusion in Effective potential inclusion in semiclassicalsemiclassical simulators simulators ((VasileskaVasileska, Ferry, Ahmed, , Ferry, Ahmed, RinghoferRinghofer -- 2002)2002)2D Poisson2D Poisson--1D Schrodinger full1D Schrodinger full--band simulator for band simulator for transport in ptransport in p--channel Si/channel Si/SiGeSiGe devices (devices (KrischnanKrischnan, , VasileskaVasileska, , FischettiFischetti –– 2005)2005)Development of 2D/3D Contact block reduction method Development of 2D/3D Contact block reduction method for quantum transport of for quantum transport of nanoscalenanoscale devices (Khan, devices (Khan, MamaluyMamaluy, , VasileskaVasileska))

PostPost--1990 Simulation Efforts1990 Simulation Efforts

Universal mobility exploration in conventionalSi inversion layers.

Exchange-correlation effects

Starined-Si inversion layers

Green’s Functions

SCHRED was one of the first toolsthat was installed on PUNCH (nanoHUB) and is the most used non-comercial simulation module.

SCHRED

Conclusions From This Work …

• We developed the first full-band self-consistent MC device simulator that takes both band-structure and quantum-mechanical size quantization effects into account.

• We find that at low applied bias there is an improved performance of p-channel strained SiGe devices due to the mobility enhancement.

• The improvement becomes degradation effect at high gate biases due to increased importance of surface-roughness and alloy disorder scattering.

Modeling p-channel SiGe Devices

ELECTRON DENSITY: DG VS. TG

0

2

4

Y [n

m]

tox

0 2 4 6 8 10 120

2

4

6

8

Z [nm]

Y [n

m] tox

2.0E

186.

0E18

1.2E

191.

8E19

2.4E

193.

0E19

3.6E

194.

2E19

<2.0

E18

>

ON-state Electron density along the dotted line

VGS=0.2, VDS=0.4V

Electron density (TG) > electron density (DG)

CBR – FinFET Simulation

0.0 0.1 0.2 0.3 0.4

0

1

2

3

4

Dra

in c

urre

nt [μ

A]

Drain voltage [V]

DG FinFET TG FinFET

VGS = 0.1V

TG FinFET stronger control of gates pronounced saturationHigh output resistance compared to DG structure

Future WorkFuture Work

On the On the semiclassicalsemiclassical arena:arena:MC device simulator for modeling Solar CellsMC device simulator for modeling Solar CellsCoupled MC simulators for electrons and phonons to Coupled MC simulators for electrons and phonons to model heating effects in model heating effects in nanoscalenanoscale devicesdevices

On the quantum arena:On the quantum arena:Coupled 3DCBR and NEMO3D code for modeling Coupled 3DCBR and NEMO3D code for modeling quantum dot quantum dot photodetectorsphotodetectors and third generation and third generation quantumquantum--dot solar cellsdot solar cellsExtend 3DCBR to include magnetic domains and Extend 3DCBR to include magnetic domains and model spin currents.model spin currents.

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