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

1. 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.

2. Outline PART 1: Introduction and methodological choices PART 2: Lattice based kinetic Monte-Carlo algorithm (HfO2) PART 3: Exploitation, validation and results

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. Why high-k oxides ? • MOSFET evolution: “scaling” Intel Corp. ITRS 2004

5. Why high-k oxides ? • 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.

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 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

7. The “Hike” project: • 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

8. Phase 1 : 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)

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. 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

11. 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

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

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

14. 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

15. Lattice based model • 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

16. Lattice based model • Substrate initialization (example) Si (100) layer (k=1) + User defined OH and siloxane distributions (random, row, or cross…) = Large variety of available substrates

17. Lattice based model • Zhuravlev model for substrate initialization From the Monte-Carlo point of view, OH density is the percentage of sites that have an OH

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. Temporal dynamics • 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

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

21. Phase 1 : Precursor Pulse - 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:

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. Mechanisms (some examples) • HfCl4 adsorption (from DFT)

24. Mechanisms (some examples) • Dissociative chemisorption (from DFT)

25. Mechanisms (some examples) • Densification mechanisms purpose

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

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

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

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

30. Hikad simulation platform • ‘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

31. Hikad simulation platform • Workspace

33. 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

34. ALD first steps • Coverage vs. substrate initialization

35. ALD first steps • 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)

36. ALD first steps • 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

37. ALD first steps • 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

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

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. 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

41. 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”)

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

43. 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”)

44. Growth kinetics: speeds 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)

45. 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

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. 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”

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 SiO2 regrowth, thanks to another existing kMC tool (Oxcad) - Dopant migration - Etching - Standardisation