1 multi-scale modeling of high-k oxides growth: kinetic monte-carlo simulation january 4 2006,...
TRANSCRIPT
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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)…
+
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