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Predictive Front-End Process Simulation Crolles, 12 April 2005 The kinetic Monte Carlo Approach Martin Jaraiz Univ. of Valladolid.

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Presentation on theme: "Predictive Front-End Process Simulation Crolles, 12 April 2005 The kinetic Monte Carlo Approach Martin Jaraiz Univ. of Valladolid."— Presentation transcript:

1 Predictive Front-End Process Simulation Crolles, 12 April 2005 The kinetic Monte Carlo Approach Martin Jaraiz Univ. of Valladolid

2 M Jaraiz, Crolles 12/04/ Acknowledgments Recent collaborators: I. Martin-Bragado P. Castrillo R. Pinacho E. Rubio C. Mok

3 M Jaraiz, Crolles 12/04/ Outline DADOS kMC: Brief history Model overview –Point defects: Charge model –Extended defects (small clusters, {311}s, Disloc. loops, Voids) –Damage, Amorphiz. & Recrystallization –Impurities & Impurity clusters –Other Materials (Oxides, Nitrides) & Interfaces –SiGe & Strain effects Next improvements Conclusions

4 M Jaraiz, Crolles 12/04/ DADOS: Brief history Summer 1994, Bell Labs : First kMC code (BLAST) was begun by Jaraiz & Gilmer. 1996, Univ. of Valladolid: Based on the accumulated experience, Jaraiz rewrote the code completely and named it DADOS to 2001: DADOS improved at the Univ. of Valladolid, and source code distributed to some Research Centers and Universities. 2001: License agreement signed with Avant! to include DADOS into Taurus TM 2002: Synopsys acquires Avant! At present, there are nearly 40 journal papers published with DADOS results (16 APL, 5 JAP, 2 PRB, 2 PRL among others)

5 M Jaraiz, Crolles 12/04/ Deep-Submicron Device PDE Solver Atomistic KMC Parameter Values: Di=0.1, Em=1.2, … Physical Models: Diffusion Clustering Amorphiz. Charge Effects Surfaces Precip./Segreg... Front-End Process Modeling

6 M Jaraiz, Crolles 12/04/ D Atomistic kMC Simulator The Atomistic KMC Approach Output Lattice Atoms are just vibrating Defect Atoms can move by diffusion hops KMC simulates Defect Atoms only

7 M Jaraiz, Crolles 12/04/ Outline DADOS kMC: Brief history Model overview –Point defects: Charge model –Extended defects (small clusters, {311}s, Disloc. loops, Voids) –Damage, Amorphiz. & Recrystallization –Impurities & Impurity clusters –Other Materials (Oxides, Nitrides) & Interfaces –SiGe & Strain effects Next improvements Conclusions

8 M Jaraiz, Crolles 12/04/ Point defects and Impurities Egap(T) + Renormalization (high doping) Nc(T), Nv(T) Fermi-Dirac statistics

9 M Jaraiz, Crolles 12/04/ Charge state update –static (immobile species) –dynamic (mobile species) Charge Effects : Implementation [I -  [I o  ___ = nini n(x) ·I-·I- I-I- nini I - (x) I + (x) x concentration I?I? n(x): from charge neutrality + Debye smoothing no interaction between repulsive species P(+x) P(-x) = exp( ) kT q·  · Electric field(  ) drift – modeled as biased diffusion:

10 M Jaraiz, Crolles 12/04/ Fermi-level Dependencies (I, V) Example: Vacancy charge states (V0,V-,V--,V+,V++) Dominant V charge state as a function of local Fermi-level e F (x,y,z)

11 M Jaraiz, Crolles 12/04/ Fermi-level Dependencies (Impurities) Example: Boron charge states mobile very fast slow

12 M Jaraiz, Crolles 12/04/ Efficient smoothing algorithms for point charges, based on charge neutrality + local Debye estimates 3D charge model (tests) With smoothing: Without:

13 M Jaraiz, Crolles 12/04/ Lines: Simulation Symbols: Continuum Intrinsic/extrinsic diffusion (tests) Extrinsic Intrinsic

14 M Jaraiz, Crolles 12/04/ Point defects: Some tests under equilibrium conditions. I and V equilibrium transport Dopant spike diffusion (boron) in equilibrium Bracht et al,98;Cowern et al, 99; Giese et al, 00 Cowern et al, 91

15 M Jaraiz, Crolles 12/04/ Outline DADOS kMC: Brief history Model overview –Point defects: Charge model –Extended defects (small clusters, {311}s, Disloc. loops, Voids) –Damage, Amorphiz. & Recrystallization –Impurities & Impurity clusters –Other Materials (Oxides, Nitrides) & Interfaces –SiGe & Strain effects Next improvements Conclusions

16 M Jaraiz, Crolles 12/04/ Extended Defects: Interstitials {311} defectsFaulted loopsPerfect loops Small clusters TEM images from Claverie et al. Cristiano et al. Cowern et al.

17 M Jaraiz, Crolles 12/04/ defects dissolution Full damage simulation: No “+N” assumption Defect cross-section automatically given by defect geometry 200 s 600 s 1200 s 1800 s 738ºC Simulation Data: Eaglesham et al.

18 M Jaraiz, Crolles 12/04/ Interstitial Supersaturation Controls dopant diffusivity Simulation Cowern et al. Data: Cowern et al.

19 M Jaraiz, Crolles 12/04/ DADOS Simulation Dislocation Loops However, {311} can in fact reach sizes >> 345 From Claverie et al. Transitition {311}  Loop: Activation Energy = 0.7 eV Loop energy < {311} energy if Number of atoms > 345 Therefore, the {311}  Loop transformation cannot be based just on minimum configurational energy.

20 M Jaraiz, Crolles 12/04/ {311} → Disloc. Loop transition {311} to dislocation loops transition. Li and Jones, 98 Lines: Simulation Symbols: Experiment {311} mean size evolution Eaglesham et al, 94 Thermally activated (0.7 eV) {311} size needs to be predicted, for correct transition to DLoop

21 M Jaraiz, Crolles 12/04/ Vacancy Clusters & Voids Agglomeration of V’s with an irregular shape (small clusters) or spherical (Voids) with the Si atomic density Binding energies for V clusters and Voids

22 M Jaraiz, Crolles 12/04/ Outline DADOS kMC: Brief history Model overview –Point defects: Charge model –Extended defects (small clusters, {311}s, Disloc. loops, Voids) –Damage, Amorphiz. & Recrystallization –Impurities & Impurity clusters –Other Materials (Oxides, Nitrides) & Interfaces –SiGe & Strain effects Next improvements Conclusions

23 M Jaraiz, Crolles 12/04/ Damage accumulation - Amorphization Implementation (3D): Accumulate I’s & V’s into Amorphous Pockets (AP). AP’s have irregular shape, like clusters. AP’s allow for dynamic anneal between cascades. AP’s activation energy (for recrystallization) is a function of AP size (equiv. number of IV pairs). Top View Cross Sect.

24 M Jaraiz, Crolles 12/04/ Lines: Simulation Symbols: Experiment Golbderg et al, 95 Amorphization: Results (I) Amorphization is predictive for a wide range of experimental conditions: Ion mass Dose rate Temperature

25 M Jaraiz, Crolles 12/04/ Amorphization is predictive for a wide range of experimental conditions: V-rich Amorphous Pockets: more free I’s (in agreement with MD) Polyatomic ions Amorphization: Results (II) Silicon Carbon sequence

26 M Jaraiz, Crolles 12/04/ DADOS Simulation Implant: 50 KeV, 3.6x10 14 Si/cm 2 (Pan et al., APL 1997) Recrystallization: Defects sequence Implementation (3D): When a box (< 2nm-side) reaches the amorphiz. threshold it becomes Amorphous. Amorphous regions in contact with the surface or with a crystalline region recrystalize with a given activation energy. Any I-V unbalance is accumulated as the amorphous region recrystallizes (and it is dumped into adjacent amorphous boxes).

27 M Jaraiz, Crolles 12/04/ Dopants have a probability to be swept by the recrystallization front. Otherwise they are left as active dopants or If they exceed the solubility limit they can be deposited as impurity clusters. Recrystallization: Impurity sweep/deposit. Amorphous layer before recrystallization SiO 2 Implant (As 2keV, cm -2 ) Annealing (700ºC, 2h)

28 M Jaraiz, Crolles 12/04/ D-Recristalization Front Amorphized region Remaining damage Arsenic (drain) Channel Velocity:

29 M Jaraiz, Crolles 12/04/ Outline DADOS kMC: Brief history Model overview –Point defects: Charge model –Extended defects (small clusters, {311}s, Disloc. loops, Voids) –Damage, Amorphiz. & Recrystallization –Impurities & Impurity clusters –Other Materials (Oxides, Nitrides) & Interfaces –SiGe & Strain effects Next improvements Conclusions

30 M Jaraiz, Crolles 12/04/ Impurity-related problems Complex relationships between: –Damage –Recrystallization: Sweep/Deposition –Charge –extended defects interactions {311}-Indium DLoops-Boron –generalized Frank-Turnbull (self-consistent) –… everything affects dopant diffusion and activation/deactivation!

31 M Jaraiz, Crolles 12/04/ Impurities: mobile species & clusters kick-out mechanism Cluster capture & emission Bi I I Frank-Turnbull mechanism

32 M Jaraiz, Crolles 12/04/ Boron (I) Pelaz et al, 97 Huang et al, 97 Boron clustering and diffusion Silicon implant, Boron spike Boron implant

33 M Jaraiz, Crolles 12/04/ Boron (II) Boron activation Pelaz et al keV, 2x10 14 cm -2 B implant 1000 s at 800ºC annealing I n B m Pathway

34 M Jaraiz, Crolles 12/04/ Arsenic Chakravarthi et al, 02 Rousseau et al, 98 Arsenic diffusion, clustering & activation/deactivation

35 M Jaraiz, Crolles 12/04/ Outline DADOS kMC: Brief history Model overview –Point defects: Charge model –Extended defects (small clusters, {311}s, Disloc. loops, Voids) –Damage, Amorphiz. & Recrystallization –Impurities & Impurity clusters –Other Materials (Oxides, Nitrides) & Interfaces –SiGe & Strain effects Next improvements Conclusions

36 M Jaraiz, Crolles 12/04/ Materials Interfaces Oxides, Nitrides: simple (B, As...) Diffusion. Impurity Trapping at the Interface Impurity Segregation into the other material

37 M Jaraiz, Crolles 12/04/ Materials Interfaces : I, V Lines: Simulation Symbols: Experiment, Cowern et al, 99 d Surface Recombination length

38 M Jaraiz, Crolles 12/04/ SiO 2 Si SiO 2 /Si: B trapping/segregation/diffusion Initial boron profile: 1x10 20 cm -3 Subsequent annealing: 2000 s at 1000 ºC Materials Interfaces : Impurities Oh-Ward model (IEDM’98): Emission Trapping

39 M Jaraiz, Crolles 12/04/ Outline DADOS kMC: Brief history Model overview –Point defects: Charge model –Extended defects (small clusters, {311}s, Disloc. loops, Voids) –Damage, Amorphiz. & Recrystallization –Impurities & Impurity clusters –Other Materials (Oxides, Nitrides) & Interfaces –SiGe & Strain effects Next improvements Conclusions

40 M Jaraiz, Crolles 12/04/ Modeling SiGe & strain Unrelaxed Si 0.8 Ge 0.2 SiO 2 /Si interface Self-Interstitials: Higher E act (I) = E form + E mig We take same E mig ⇒ Higher E form ⇒ Lower I conc.

41 M Jaraiz, Crolles 12/04/ B, As Diffusion in strained SiGe Initial uniform conc. 1e19 cm -3 Lower E act (V) Higher V conc. Lower B diffusivity Higher E act (I) Lower I conc. Higher As diffusivity

42 M Jaraiz, Crolles 12/04/ D-Atomistic Simulation in Taurus TPA (Taurus Process Atomistic) uses DADOS to perform atomistic simulation of diffusion and defects. Taurus handles the rest of the processing (deposition, etching…) in the conventional way. STI Spacer Gate Drain Source

43 M Jaraiz, Crolles 12/04/ Dopants and Defects in DADOS Drain Source Arsenic {311}s Boron

44 M Jaraiz, Crolles 12/04/ {311} Extended Defects: Zoom-In Plenty of defects in source, but just a few in drain

45 M Jaraiz, Crolles 12/04/ What can be improved next? Other dopants: C,P,F,In… (mechanisms ready, calibration needed) Efficiency (CPU time and memory) Flexibility in the models: Models defined by the user (partially done) Diffusion in amorphous layers Moving boundaries (materials interfaces). Include Si-Ge alloys (in progress). Include stress (in progress).

46 M Jaraiz, Crolles 12/04/ Conclusions The strength of kMC is that it can handle many mechanisms simultaneously, as needed in complex processing. As a consequence it can be highly predictive, as already shown in several publications. It is not meant to compete in accuracy with ad-hoc simulation approaches, ‘tabulated’ for specific conditions. The goal, instead, is to attain a kMC simulator that, although not highly accurate for any particular simulation, it never gives a totally wrong result, even for previously unexplored simulation conditions. kMC is a predictive process simulation technique, intended to replace time/money costly experiments to explore unknown new conditions, for which conventional simulators can be highly unreliable.


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