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Multi-scale modeling of High-k oxides growth:

kinetic Monte-Carlo simulation

January 4 2006, LAAS-CNRS, Toulouse.

Guillaume MAZALEYRAT

Ph-D supervisors: Alain ESTEVE & Mehdi DJAFARI-ROUHANI

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Outline

PART 1:Introduction and methodological choices

PART 2:

Lattice based kinetic Monte-Carlo algorithm (HfO2)

PART 3:Exploitation, validation and results

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PART 1

Introduction and methodological choices

High-k oxides: Why? How? Methodology: available approaches overview Multi-scale strategy The “Hike” project

Our goal: first predictive and generic kMC tool for high-k oxides deposition (ALD first steps, kinetics, process optimization…)

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Why high-k oxides ?

MOSFET evolution: “scaling”

Production year

Etching width

Gate oxide

thickness

1997 250 nm 4 – 5 nm

1999 180 nm 3 – 4 nm

2001 150 nm 2 – 3 nm

2002 130 nm 2 – 3 nm

2004 90 nm < 1.5 nm

2007 65 nm < 0.9 nm

2010 45 nm < 0.7 nmITRS 2004

Intel Corp.

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Problem: high leakage current through the gate.

A solution: use a gate oxide of greater permittivity than SiO2.

Oxide k

SiO2 3,9

Al2O3 ~ 9,8

ZrO2 ~25

HfO2 ~35

0k SC

t

Why high-k oxides ?

To extend Moore’s Law

Intel Corp.

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High-k oxides implementation into microelectronics Materials properties considerations

-High permittivity-Sufficient band offset (to minimize leakage)-Low fix charges density (for reliable threshold voltage)-Low interface states density (to keep an acceptable mobility in the channel)-Low dopant diffusivity (to keep them in the electrode or the channel)-Limitation of SiO2 regrowth (which would reduce the capacitance)-Amorphous phase or at least few grain boundaries (to minimize leakage)

Process considerations-Known solution for the gate electrode-High-k oxide deposition process compatibility (with other materials, with industrial needs)-High-k oxide (itself) compatibility with other CMOS processes (e.g. crystallization problems, dopant diffusivity)-Reproducibility-Reliability

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NMRC/Tyndall, Ireland (S. Elliott):DFT/mechanisms

Motorola/Freescale, Germany (J. Schmidt):DFT/mechanisms, molecular dynamics, rate equations

University College London, United Kingdom (A. Schluger, J. Gavartin):interface, defects, dopant diffusivity

Infineon, Germany (A. Kersch):reactor scale and feature scale simulations

LAAS-CNRS (G. Mazaleyrat, A. Estève, M. Djafari-Rouhani, L. Jeloaica): DFT/mechanisms, kinetic Monte-Carlo

New simulation tools for High-k oxides growth: Atomic Layer Deposition of HfO2, ZrO2, Al2O3

The “Hike” project:

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High-k oxides implementation into microelectronics Process choice: Atomic Layer Deposition (ALD)

Phase 1 :Precursor pulse

Phase 2 :Precursor purge

Phase 3 :Water pulse

Phase 4 :Water purge

(…)

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Methodology: available approaches overview

Available experimental data:

IR spectroscopy, X-ray photoelectron spectroscopy (XPS), Auger electron spectroscopy (AES), low energy ion scattering (LEIS)…

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Macroscopic simulations:

feature scale and reactor scale.

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Multi-scale strategy Microscopic – Mesoscopic - Macroscopic

ab initio / DFT / MD Kinetic Monte-Carlo

About 100 atoms

Time scale: picoseconds

Up to millions of atoms

Time scale: seconds

Characterization,process,

technology…

Experimentation, Macroscopic simulations

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PART 2

Lattice based kinetic Monte-Carlo algorithm (HfO2)

Preliminary considerations: space and time scales Lattice based model: how the atomistic configuration is described Temporal dynamics: how the atomistic configuration changes Elementary mechanisms: some examples

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Preliminary considerations:

Space scale: lattice based model

≈ ≈

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Preliminary considerations:

Time scale: simulation algorithm choice

TIME CONTINUOUS KINETIC MONTE-CARLO

Attainable phenomenon duration: second

Realistic evolution

Monte-Carlo steps have time meaning

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Lattice based model Merging different structures into one framework

Conventional HfO2 cell on substrate Discrete locating model

Si (layer k=1) Hf (k=2 and even layers)

Ionic oxygen (k + 1/2) Hf (k=3 and odd layers)

2D cell

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Other aspects: strands, contaminants…

Lattice based model

Example: non-crystalline HfCl3 group, bound to the substrate via one oxygen atom. Non-crystalline aspects:

-Non-crystalline Hf

-Non-crystalline O

-OH strands

-Cl strands

-HCl contamination

-H2O

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Substrate initialization (example)

Lattice based model

Si (100) layer (k=1)

+

User defined OH and siloxane distributions

(random, row, or cross…)

=

Large variety of available substrates

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Zhuravlev model for substrate initialization

Lattice based model

From the Monte-Carlo point of view, OH density is the percentage of sites that have an OH

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Temporal dynamics Mechanisms and events (definitions)

Mechanism = elementary reaction mechanism with associated activation barrier E≠

Site = one cell within the lattice, located by (i,j,k) indexes and containing occupation fields (can be empty)

Event = Mechanism + Site, (depending on the local occupation, can be possible or not, thus must be “filtered”)

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Acceptances and occurrence times calculation

i, j,k,m

m

log Zt

where Z is a random number between 0 and 1

mm

B

E.exp

k T

Maxwell-Boltzmann statistics derivedacceptance for arrival mechanisms

(1-precursor and 2-water):

T.M

S.P.Cst

2,1

2,1

Occurrence time of event « mechanism m on site (i,j,k) », if possible :

Arrhenius law derived acceptance with attempt frequency ν

for all other mechanisms:

Temporal dynamics

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Summary: the kinetic Monte-Carlo cycle

Occurrence timescalculation

and comparison

Atomisticconfiguration

change

Events filtering

Occurrence of the event of smallest occurrence time

Temporal dynamics

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ALD cycle + kMC cycle

Phase 1 : Precursor Pulse- duration T1- temperature Th1 -pressure P1

Phase 2 : Precursor Purge- duration T2- temperature Th2

Phase 3 : Water Pulse- duration T3- temperature Th3- pressure P3

Phase 4 : Water Purge- duration T4- temperature Th4

As the kMC cycle works, ALD parameters change periodically:

Temporal dynamics

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Mechanisms: complete list01 MeCl4 adsorption02 H2O adsorption03 MeCl4 Desorption04 HCl Production05 H2O Desorption06 Hydrolysis07 HCl Recombination08 HCl Desorption09 Dens. Inter_CI_1N_cOH-iOH (all k)10 Dens. Inter_CI_1N_cOH-iCl (all k)11 Dens. Inter_CI_1N_cCl-iOH (all k)12 Dens. Inter_CI_2N_cOH-iOH (all k not2)13 Dens. Inter_CI_2N_cOH-iCl (all k not2)14 Dens. Inter_CI_2N_cCl-iOH (all k not2)15 Dens. Intra_CI_1N_cOH-iOH (k=2)16 Dens. Intra_CI_1N_cOH-iCl (k=2)17 Dens. Intra_CI_1N_cCl-iOH (k=2)18 Dens. Intra_CC_1N_cOH-cOH (k=2)19 Dens. Intra_CC_1N_cOH-cCl (k=2)20 Dens. Intra_CC_2N_cOH-cOH (k=2)21 Dens. Intra_CC_2N_cOH-cCl (k=2)22 Dens. Bridge_TI_2N_tOH-iOH (k=2)23 Dens. Bridge_TI_2N_tOH-iCl (k=2)24 Dens. Bridge_TI_2N_tCl-iOH (k=2)

25 Dens. Bridge_TI_3N_tOH-iOH (k=2)26 Dens. Bridge_TI_3N_tOH-iCl (k=2)27 Dens. Bridge_TI_3N_tCl-iOH (k=2)28 Dens. Bridge_TC_3N_tOH-cOH (k=2)29 Dens. Bridge_TC_3N_tOH-cCl (k=2)30 Dens. Bridge_TC_3N_tCl-cOH (k=2)31 Dens. Bridge_TC_4N_tOH-cOH32 Dens. Bridge_TC_4N_tOH-cCl33 Dens. Bridge_TC_4N_tCl-cOH34 Dens. Bridge_TT_3N_tOH-tOH (k=2)35 Dens. Bridge_TT_3N_tOH-tCl (k=2)36 Dens. Bridge_TT_4N_tOH-tOH37 Dens. Bridge_TT_4N_tOH-tCl38 Dens. Bridge_TT_5N_tOH-tOH39 Dens. Bridge_TT_5N_tOH-tCl40 Siloxane Bridge Opening

Suggested by…-DFT studies-kMC investigation-Experiments

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Mechanisms (some examples) HfCl4 adsorption (from DFT)

E≠ = 0 eV

ΔE = -0.48 eV

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Mechanisms (some examples) Dissociative chemisorption (from DFT)

E≠ = 0.88 eV

ΔE = 0.26 eV

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Mechanisms (some examples) Densification mechanisms purpose

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Mechanisms (some examples) Densification: interlayer non-cryst./cryst. (from kMC)

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Mechanisms (some examples) Densification: multilayer non-cryst./tree (from kMC)

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Mechanisms (some examples) Siloxane bridge opening (from experiments)

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PART 3

Exploitation, validation and results

Hikad simulation platform ALD first steps Growth kinetics: transient regime Growth kinetics: steady state regime

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‘Hikad’ = simulation application ‘kmc’ + analysis application ‘anl’

Written in Fortran90 Running on Linux (kernel 2.6) Using ‘AtomEye’, free atomistic configuration

viewer: http://alum.mit.edu/www/liju99/Graphics/A Ref: J. Li, Modelling Simul. Mater. Sci. Eng. 11 (2003) 173

Hikad simulation platform

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Workspace

Hikad simulation platform

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Hikad simulation platform Main features• ZrO2, HfO2 and Al2O3 ALD• ALD thermodynamic parameters (link with experimental data)• Start from an existing atomistic configuration file (Recovery option)• Initial substrate atomistic configuration customization• Feedback options (log file + automatic configuration/graphic files export)• Back up option

Evolutivity• Steric restriction switch (for big precursors)• Mechanisms activation energies

Performance• Huge substrates compared to ab initio or DFT• Up to 1015 events• Improved events filtering (SmartFilter option)• Shortcuts method preventing fast flip back events (SmartEvents option)• Computation effectiveness analysis

Analysis• Simulation data analysis, even during simulation job• Easy and fast browsing through events using bookmarks (find event, ALD phase, ALD cycle...)• Atomistic configuration visualisation using AtomEye• Snapshots (jpeg, ps or png formats)• Configuration analysis (substrate, coverage, coordination...)• Batch processing

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ALD first steps Coverage vs. substrate initialization

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Coverage vs. substrate initialization

ALD first steps

One precursor pulse phase:100ms, 1.33mbar, 300°C

-Best start substrates: 50% and Random on dimers-Crystallinity seems too high (because of 0.5eV barrier)

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Early densifications barrier fit

ALD first steps

One precursor pulse phase:90% OH, 200ms, 1.33mbar, 300°C

Criteria: 90% OH => 80% coverage (exp.)=> Densifications barriers: 1.5 eV

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Coverage vs. Deposition temperature

ALD first steps

Precursor pulse phase:50ms, 1.33mbar + purge

-Low temperatures: chemisorptions can’t occur-High temperatures: poor OH density=> Optimal temperature: 300°C

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Surface saturation

ALD first steps

One precursor pulse phase:1.33mbar, 300°C

Saturation: 48% coverage for a 90ms long pulse

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Growth kinetics: transient regime Coverage for 10 ALD cycles

Pulse phases: 1.33mbar, 300°C+ purges

Fast first cycle, then slow growth…73% coverage saturation = simulation artefact

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Siloxane bridge opening barrier fit

Growth kinetics: transient regime

800ms water pre-treatmentthen: 50ms precursor pulse1.33mbar, 300°C

OH density increase => higher coverage after precursor pulseExperimental fit => siloxane bridge opening barrier = 1.3eV

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End configuration

Growth kinetics: transient regime

-Poor crystallinity for first layer-High cristalinity above-Poor crystallinity and filling on top because of “blocking states” (simulation artefact)

-First layer will never be full nor dense: bridge densifications needed-Hard to achieve 100% substrate coverage, “waiting” for SiOSi openings-“Blocking states” are visible (“trees”)

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Start configuration for steady state regime

Growth kinetics: steady state regime

HfO(OH)2

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End configuration

Growth kinetics: steady state regime

-Very high crystallinity for most of layers-Again: poor crystallinity and filling on top because of “blocking states” (simulation artefact)

-Growth works better (no waiting effect)-“Blocking states” are visible (“trees”)

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Growth kinetics: speeds

Transient regime Steady state regime

Vt,exp = 7E+13 Hf/cm²/cycle (TXRF) Vs,exp = 12E+13 Hf/cm²/cycle (TXRF)

Hard to obtain a reliable and stable growth speed because of blocking effectSteady state regime simulations suffer less

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Growth kinetics: conclusions

ALD cycle

Transient regime (Vt)

“Waiting” for siloxane bridges openings until full SiO2

coverage.

Steady state regime (Vs>Vt)

HfO2 growth onto HfOx(OH)y (more OH)

Am

ount

of

depo

site

d H

f at

oms

1st cycle

Fast initial Si-OH sites saturation

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Conclusion

Original methodology:- Multi-scale strategy- First predictive tool at these space and time scales for high-k oxides growth- Link between atomic scale considerations and industrial needs for process optimisation

Lattice based time continuous kinetic Monte-Carlo algorithm:- Lattice based => millions of atoms- Time continuous kMC => process duration- Non-crystalline aspects: strands, contaminant, densification issues…- Large initial substrates variety- Each Monte-Carlo step has time meaning (variable duration)- ALD process parameters (phases, duration, pressure, temperatures)- Elementary mechanisms (suggested by DFT or kMC or Experiment)

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Conclusion

Exploitation:- Hikad simulation platform- Powerful, flexible and “user friendly” Analysis tool (events browsing, atomistic viewer, batch analysis…)- Generic method: MeO2 oxides (changing barriers), other precursors (using steric restriction switch)

Validation and first encouraging results:- Substrate preparation dependence- Optimal growth temperature- Surface saturation- Activation barriers calibration (densifications and siloxane bridge opening)- Growth kinetics: two growth regimes, hard substrate coverage, but “blocking effect”

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Perspectives…

First:- Reduce blocking effect with new densification mechanisms- Add migration mechanisms, and lateral growth mechanisms to obtain complete substrate coverage and maybe grain boundaries- Study coordination evolution and crystallisation- Optimisation: keep on event smart filtering, add shortcuts procedure for water based mechanisms, maybe Kawasaki generic barriers for future simple mechanisms

Next:- Simulate thermal annealing (migrations, crystallisation…)- Study interfacial SiO2 regrowth, thanks to another existing kMC tool (Oxcad)- Dopant migration- Etching- Standardisation