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Multi-scale modeling of High-k oxides growth: kinetic Monte-Carlo simulationPowerPoint Presentation

Multi-scale modeling of High-k oxides growth: kinetic Monte-Carlo simulation

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Multi-scale modeling of High-k oxides growth: kinetic Monte-Carlo simulation

Guillaume MAZALEYRAT

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

January 4 2006, LAAS-CNRS, Toulouse.

Outline

PART 1:

Introduction and methodological choices

PART 2:

Lattice based kinetic Monte-Carlo algorithm (HfO2)

PART 3:

Exploitation, validation and results

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…)

- To extend Moore’s Law

Problem: high leakage current through the gate.

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

Intel Corp.

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

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

- 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

Precursor pulse

Phase 2 :

Precursor purge

Phase 3 :

Water pulse

Phase 4 :

Water purge

(…)

High-k oxides implementation into microelectronics

- Process choice: Atomic Layer Deposition (ALD)

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.

Experimentation, Macroscopic simulations

Kinetic Monte-Carlo

ab initio / DFT / MD

Characterization,

process,

technology…

About 100 atoms

Time scale: picoseconds

Up to millions of atoms

Time scale: seconds

Multi-scale strategy

- Microscopic – Mesoscopic - Macroscopic

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

- Time scale: simulation algorithm choice

TIME CONTINUOUSKINETICMONTE-CARLO

Attainable phenomenon duration: second

Realistic evolution

Monte-Carlo steps have time meaning

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

Lattice based model

- Merging different structures into one framework

- Other aspects: strands, contaminants…

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

- Substrate initialization (example)

Si (100) layer (k=1)

+

User defined OH and siloxane distributions (random, row, or cross…)

=

Large variety of available substrates

- Zhuravlev model for substrate initialization

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

- 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”)

- Acceptances and occurrence times calculation

Arrhenius law derived acceptance with attempt frequency ν

for all other mechanisms:

Maxwell-Boltzmann statistics derived

acceptance for arrival mechanisms

(1-precursor and 2-water):

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

where Z is a random number between 0 and 1

Occurrence times

calculation

and comparison

Atomistic

configuration

change

Temporal dynamics

- Summary: the kinetic Monte-Carlo cycle

Occurrence of the event of smallest occurrence time

- duration T1

- temperature Th1

-pressure P1

Phase 4 : Water Purge

- duration T4

- temperature Th4

Phase 2 : Precursor Purge

- duration T2

- temperature Th2

Phase 3 : Water Pulse

- duration T3

- temperature Th3

- pressure P3

Temporal dynamics

- ALD cycle + kMC cycle

As the kMC cycle works, ALD parameters change periodically:

01 MeCl4 adsorption

02 H2O adsorption

03 MeCl4 Desorption

04 HCl Production

05 H2O Desorption

06 Hydrolysis

07 HCl Recombination

08 HCl Desorption

09 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-cOH

32 Dens. Bridge_TC_4N_tOH-cCl

33 Dens. Bridge_TC_4N_tCl-cOH

34 Dens. Bridge_TT_3N_tOH-tOH (k=2)

35 Dens. Bridge_TT_3N_tOH-tCl (k=2)

36 Dens. Bridge_TT_4N_tOH-tOH

37 Dens. Bridge_TT_4N_tOH-tCl

38 Dens. Bridge_TT_5N_tOH-tOH

39 Dens. Bridge_TT_5N_tOH-tCl

40 Siloxane Bridge Opening

Suggested by…

-DFT studies

-kMC investigation

-Experiments

- HfCl4 adsorption (from DFT)

- Dissociative chemisorption (from DFT)

- Densification mechanisms purpose

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

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

- Siloxane bridge opening (from experiments)

Exploitation, validation and results

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

- ‘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

- Workspace

- 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

- Coverage vs. substrate initialization

- Coverage vs. substrate initialization

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)

- Early densifications barrier fit

One precursor pulse phase:

90% OH, 200ms, 1.33mbar, 300°C

Criteria: 90% OH => 80% coverage (exp.)

=> Densifications barriers: 1.5 eV

- Coverage vs. Deposition temperature

Precursor pulse phase:

50ms, 1.33mbar + purge

-Low temperatures: chemisorptions can’t occur

-High temperatures: poor OH density

=> Optimal temperature: 300°C

- Surface saturation

One precursor pulse phase:

1.33mbar, 300°C

Saturation: 48% coverage for a 90ms long pulse

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

Growth kinetics: transient regime

- Siloxane bridge opening barrier fit

800ms water pre-treatment

then: 50ms precursor pulse

1.33mbar, 300°C

OH density increase => higher coverage after precursor pulse

Experimental fit => siloxane bridge opening barrier = 1.3eV

Growth kinetics: transient regime

- End configuration

-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”)

Growth kinetics: steady state regime

- End configuration

-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”)

Hard to obtain a reliable and stable growth speed because of blocking effect

Steady state regime simulations suffer less

Transient regime

Steady state regime

Vt,exp = 7E+13 Hf/cm²/cycle (TXRF)

Vs,exp = 12E+13 Hf/cm²/cycle (TXRF)

1st cycle

Fast initial Si-OH sites saturation

Steady state regime (Vs>Vt)

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

Amount of deposited Hf atoms

Transient regime (Vt)

“Waiting” for siloxane bridges openings until full SiO2 coverage.

ALD cycle

Growth kinetics: conclusions

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)

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”

- 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

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