1. 1 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

2. 2 Outline PART 1: Introduction and methodological choices PART 2: Lattice based kinetic Monte-Carlo algorithm (HfO 2 ) PART 3: Exploitation, validation and results

3. 3 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…)

4. 4 Why high-k oxides ? MOSFET evolution: “scaling” Production year Etching width Gate oxide thickness 1997250 nm4 – 5 nm 1999180 nm3 – 4 nm 2001150 nm2 – 3 nm 2002130 nm2 – 3 nm 200490 nm< 1.5 nm 200765 nm< 0.9 nm 201045 nm< 0.7 nm ITRS 2004 Intel Corp.

5. 5 Problem: high leakage current through the gate. A solution: use a gate oxide of greater permittivity than SiO 2. Oxidek SiO 2 3,9 Al 2 O 3 ~ 9,8 ZrO 2 ~25 HfO 2 ~35 Why high-k oxides ? To extend Moore’s Law Intel Corp.

6. 6 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 SiO 2 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

7. 7 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 HfO 2, ZrO 2, Al 2 O 3 The “Hike” project:

8. 8 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 (…)

9. 9 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.

10. 10 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

11. 11 PART 2 Lattice based kinetic Monte-Carlo algorithm (HfO 2 ) 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

12. 12 Preliminary considerations: Space scale: lattice based model ≈≈

13. 13 Preliminary considerations: Time scale: simulation algorithm choice TIME CONTINUOUS KINETIC MONTE-CARLO Attainable phenomenon duration: second Realistic evolution Monte-Carlo steps have time meaning

14. 14 Lattice based model Merging different structures into one framework Conventional HfO 2 cell on substrateDiscrete 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

15. 15 Other aspects: strands, contaminants… Lattice based model Example: non-crystalline HfCl 3 group, bound to the substrate via one oxygen atom. Non-crystalline aspects: -Non-crystalline Hf -Non-crystalline O -OH strands -Cl strands -HCl contamination -H 2 O

16. 16 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

17. 17 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

18. 18 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”)

19. 19 Acceptances and occurrence times calculation where Z is a random number between 0 and 1 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 : Arrhenius law derived acceptance with attempt frequency ν for all other mechanisms: Temporal dynamics

20. 20 Summary: the kinetic Monte-Carlo cycle Occurrence times calculation and comparison Atomistic configuration change Events filtering Occurrence of the event of smallest occurrence time Temporal dynamics

21. 21 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

22. 22 Mechanisms: complete list 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

23. 23 Mechanisms (some examples) HfCl 4 adsorption (from DFT) E ≠ = 0 eV ΔE = -0.48 eV

24. 24 Mechanisms (some examples) Dissociative chemisorption (from DFT) E ≠ = 0.88 eV ΔE = 0.26 eV

25. 25 Mechanisms (some examples) Densification mechanisms purpose

26. 26 Mechanisms (some examples) Densification: interlayer non-cryst./cryst. (from kMC)

27. 27 Mechanisms (some examples) Densification: multilayer non-cryst./tree (from kMC)

28. 28 Mechanisms (some examples) Siloxane bridge opening (from experiments)

29. 29 PART 3 Exploitation, validation and results Hikad simulation platform ALD first steps Growth kinetics: transient regime Growth kinetics: steady state regime

30. 30 ‘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 http://alum.mit.edu/www/liju99/Graphics/A Hikad simulation platform

31. 31 Workspace Hikad simulation platform

32. 32

33. 33 Hikad simulation platform Main features ZrO 2, HfO 2 and Al 2 O 3 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 10 15 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

34. 34 ALD first steps Coverage vs. substrate initialization

35. 35 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)

36. 36 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

37. 37 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

38. 38 Surface saturation ALD first steps One precursor pulse phase: 1.33mbar, 300°C Saturation: 48% coverage for a 90ms long pulse

39. 39 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

40. 40 Siloxane bridge opening barrier fit Growth kinetics: transient regime 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

41. 41 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”)

42. 42 Start configuration for steady state regime Growth kinetics: steady state regime HfO(OH) 2

43. 43 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”)

44. 44 Growth kinetics: speeds Transient regimeSteady state regime V t,exp = 7E+13 Hf/cm²/cycle (TXRF)V s,exp = 12E+13 Hf/cm²/cycle (TXRF) Hard to obtain a reliable and stable growth speed because of blocking effect Steady state regime simulations suffer less

45. 45 Growth kinetics: conclusions ALD cycle Transient regime (V t ) “Waiting” for siloxane bridges openings until full SiO 2 coverage. Steady state regime (V s >V t ) HfO 2 growth onto HfO x (OH) y (more OH) Amount of deposited Hf atoms 1 st cycle Fast initial Si-OH sites saturation

46. 46 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)

47. 47 Conclusion Exploitation: - Hikad simulation platform - Powerful, flexible and “user friendly” Analysis tool (events browsing, atomistic viewer, batch analysis…) - Generic method: MeO 2 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”

48. 48 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 SiO 2 regrowth, thanks to another existing kMC tool (Oxcad) - Dopant migration - Etching - Standardisation