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Feature-scale to wafer-scale modelling and simulation of physical vapor deposition

Feature-scale to wafer-scale modelling and simulation of physical vapor deposition

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Feature-scale to wafer-scale modelling and simulation of physical vapor deposition

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  1. Feature-scale to wafer-scale modelling and simulation of physical vapor deposition Peter O’Sullivan work done with: Frieder Baumann, George Gilmer & Jacques Dalla Torre, Bell Labs., Lucent Technologies, Murray Hill, NJ In collaboration with: I. Petrov, C.-S. Shin and T.-Y. Lee Materials Research Lab, U. of Illinois, Urbana-Champaign Funded by an NSF/DARPA VIP grant through the University of Illinois

  2. Background

  3. Multi-level interconnects / metallization for ICs Tungsten (W) deposited in circular “vias” (plugs) using CVD Al lines (Cu electro-deposited in long trenches)

  4. Cu diffuses into Si short circuit • WF6 + 3H2O  W + 3O + 6HF etches SiO2 • duringCVD fill of vias 2mm Must use “barrier” layers of Ti, TiN, Ta, TaN to to prevent diffusion or etch-damage Cu Ta SiO2 Si Thin Films for Metalization

  5. Simulation of PVD into trench Keyhole formation Low side-wall coverage Low bottom coverage

  6. Micro-voids and grain boundaries impinging atoms ~ 0.25mm 10nm Barrier failure • Metallic films are polycrystalline Columnar (rough) growth and pores more likely because of oblique incidence& low surface diffusivity ( Monte Carlo simulations by Jacques Dalla Torre & George Gilmer )

  7. Objectives: 1. Predict film coverage across wafer 2. Optimize deposition process

  8. Axisymmetric vias: • Validation + analytic scaling with AR • Different angular distributions • Comparison with experiment (Ti and Ta) • General 3D: • Across-wafer non-uniformity • Modelling issues • Problems, challenges • Summary & conclusions Talk Outline • Physical model of low pressure PVD: • Feature-scale + reactor-scale • (continuum) (atomistic) • Numerics for moving interface: • Level sets

  9. sputter target Ti, Ta, Al, Cu, .... S N N S -V +V +V Ar+ plasma Ar P ~ 1 - 20 mTorr wafer 30 cm Low pressure PVD—DC magnetron sputtering Rotating magnetic field “traps” electrons => non-uniform target erosion

  10. Must calculate flux at each surface point • Target visibility/shadowing.................Ray tracing • Need to know: • Size and distance of target • Target erosion pattern (relative sputter rate across target) • Angular distribution of atoms from target, f(q) • Current assumption / applicability: • Sticking coeff. = 1 ..................... Ti, Ta • More complex surface kinetics under development • (reflection, resputtering etc.) Sputter Target bL bR n d Feature on wafer Physical Model of Sputter Deposition Advance using level sets

  11. Continuum Modeling • Objectives: • Compute bottom / sidewall step coverage in high aspect • ratio trenches, vias, etc. • Predict across-wafer non-uniformity of coverage • — Simulate feature-scale film profile evolution in 3D • Study effects of macroscopic reactor variables on coverage • — target erosion • — angular distribution of different materials • — gas pressure • Incorporate important physical effects as determined from • complementary Monte Carlo simulators and experimental • data • Develop efficient algorithms for O(N4—5) ray-tracing codes

  12. Binary collision MC code gives resultant angular distribution, f(q), just above wafer f(q) then used in level set code “virtual” target Low pressure PVD — Monte Carlo vapor transport code Rotating magnetic field “traps” electrons sputter target Ti, Ta, Al, Cu, .... -V S N N S +V plasma Ar+ +V Ar P ~ 1 - 20 mTorr wafer

  13.  w(r) f(q) cos g F3D(substrate) = dA r2 visible region DA discrete surface element on target f(q) Deposition rate given by: q r n 1.2 3D MD data for Al g Can use different angular distibutions: Nonlinear curve fit 1 Equivalent 2D flux f(q) = cos(q)(isotropic emission from target) f(q) = f(q) =· · · · · Cosq discrete surface element on substrate 0.8 w(r) = weight function from target erosion profile 0.6 ......from molecular dynamics calculations 0.4 q 0.2 ......Monte Carlo vapor transport code 0 0 10 20 30 40 50 60 70 80 90 q (deg) Computation of geometric 3D material flux

  14. Code / model validation

  15. 2R q Z wafer g h w Via Geometry • 3D flux • finite target • 3D line-of- • sight model • Axisymmetric, but • with 3D shadowing AR = h / w Q = Z / R

  16. } Field = 250 Å } Field = 1250 Å BC = 100 b / t SWB = 100 s / t t s b Step coverage vs. AR : Circular Via Analytic AR = h / w Q = Z / R Analytic Bottom coverage ~AR–2 Side-wall coverage ~AR–3

  17. 1.2 Subcosine (ellipse) * 1.0 cosine Polar plot: 0.8 dN — 0.6 W d 0.4 Ti at 2mTorr (Varian M2000) MC vapor transport code 0.2 0.0 0 20 40 60 80 q (deg) Ti deposition into vias (which angular distribution?) * suggested by Malaurie & Bessaudou (Thin Solid Films v. 286, 1996)

  18. Ti into vias Deposition Start End cosine HRSEM f(q) from gas transport code Experimental data Subcosine (ellipse) BC vs AR for several angular distributions • Subcosine shows best agreement  subcosine + scattering

  19. Full 3D — Across-wafer non-uniformity

  20. cut-away viewfrom below cut-away side view Complex 3D features 20cm wafer; 30cm target; depth = 0.8mm; AR = 2;deposited 0.4mm

  21. 0.4 mm Target Plan view z wafer y z (mm) xoff x x y Off-axis circular via, depth = 0.85mm, aspect ratio, AR = 2.0, deposited 0.3mm Asymmetry in minimum step coverage ~ 10% LHS: Sees less of target LHS RHS: Shadowed by overhang Off-Axis Deposition

  22. More experimental validation — long-throw deposition (similar to ionized PVD)

  23. R 3 cm • Measured target erosion profile • modelled by w(r) 1.2 1.0 1.0 w(r) 0.8 • Simulation takes angular distribution • from vapor transport code dN — dW 0.6 ZT = 10 cm 0.4 P=1mTorr 0.2 0.0 r(cm) 20 40 60 80 q 0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 r Low pressure Ta PVD (circular via) cosine

  24. R 3 cm ZT = 10 cm P = 1mTorr Experimental Cosine (no erosion) Erosion + scattering r Low pressure Ta PVD (circular via)

  25. R 3 cm ZT = 10 cm P = 1mTorr Amplitude = 4 D Amplitude = 8 D r D = 0.0025 mm (400 X 400) Columnar growth / roughness

  26. Level set codeÞfast, accurate, predictive model for PVD • of refractory metals • LS code coupled to MC code throughf(q)and “virtual” target • Validated LS code using analytic formulae • — Step coverage ~ AR–2 (trench) • — Step coverage ~ AR–3 (via) • Quantitative comparison w/ experiment • Ti data: Subcosine distribution improves agreement —Need more data for ang. dist. + vapor transport • Ta data: Can predict bottom coverage —Need to incorporate more physics to predict closing of feature (breadloafing) • Full 3D code • Strong non-uniformity in coverage across wafer Conclusions