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Feature-scale to wafer-scale modelling and simulation of physical vapor deposition Peter O’Sullivan Funded by an NSF/DARPA VIP grant through the University of Illinois In collaboration with: I. Petrov, C.-S. Shin and T.-Y. Lee Materials Research Lab, U. of Illinois, Urbana-Champaign work done with: Frieder Baumann, George Gilmer & Jacques Dalla Torre, Bell Labs., Lucent Technologies, Murray Hill, NJ
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Background
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Multi-level interconnects / metallization for ICs Tungsten (W) deposited in circular “vias” (plugs) using CVD Al lines (Cu electro- deposited in long trenches)
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Thin Films for Metalization Cu Ta SiO 2 Si WF 6 + 3H 2 O W + 3O + 6 HF etches SiO 2 during CVD fill of vias Cu diffuses into Si short circuit Must use “barrier” layers of Ti, TiN, Ta, TaN to to prevent diffusion or etch-damage 2m2m
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Simulation of PVD into trench Low bottom coverage Keyhole formation Low side-wall coverage
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Barrier failure Metallic films are polycrystalline Micro-voids and grain boundaries Columnar (rough) growth and pores more likely because of oblique incidence & low surface diffusivity 10nm impinging atoms ~ 0.25 m ( Monte Carlo simulations by Jacques Dalla Torre & George Gilmer )
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Objectives: 1. Predict film coverage across wafer 2. Optimize deposition process
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Talk Outline Physical model of low pressure PVD: Feature-scale + reactor-scale (continuum) (atomistic) Axisymmetric vias: Validation + analytic scaling with AR Different angular distributions Comparison with experiment (Ti and Ta) Summary & conclusions General 3D: Across-wafer non-uniformity Modelling issues P roblems, challenges Numerics for moving interface: Level sets
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Low pressure PVD—DC magnetron sputtering Rotating magnetic field “traps” electrons => non-uniform target erosion sputter target Ti, Ta, Al, Cu,.... +V SN SN wafer -V-V Ar + Ar P ~ 1 - 20 mTorr +V plasma 30 cm
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Target Feature on wafer Sputter LL RR n Need to know: Size and distance of target Target erosion pattern (relative sputter rate across target) Angular distribution of atoms from target, f( ) Must calculate flux at each surface point Target visibility/shadowing.................Ray tracing Current assumption / applicability: Sticking coeff. = 1..................... Ti, Ta More complex surface kinetics under development (reflection, resputtering etc.) Physical Model of Sputter Deposition Advance using level sets
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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(N 4—5 ) ray-tracing codes Continuum Modeling
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Low pressure PVD — Monte Carlo vapor transport code SN SN wafer sputter target Rotating magnetic field “traps” electrons -V-V Ar + Ar Ti, Ta, Al, Cu,.... P ~ 1 - 20 mTorr +V plasma +V Binary collision MC code gives resultant angular distribution, f( ), just above wafer f( ) then used in level set code “virtual” target
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Computation of geometric 3D material flux 0 0.2 0.4 0.6 0.8 1 1.2 0102030405060708090 (deg) 3D MD data for Al Nonlinear curve fit Equivalent 2D flux Cos f( AA g q r discrete surface element on target discrete surface element on substrate n Deposition rate given by: w( ) f( ) cos r 2r 2 dA visible region F 3D ( substrate ) = w( ) = weight function from target erosion profile f( cos( (isotropic emission from target) f( f( ......from molecular dynamics calculations Can use different angular distibutions:......Monte Carlo vapor transport code
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Code / model validation
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Via Geometry 3D flux finite target 3D line-of- sight model Axisymmetric, but with 3D shadowing AR = h / w Q = Z / R 2R h w Z wafer
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Step coverage vs. AR : Circular Via Side-wall coverage Analytic Bottom coverage AR = h / w Q = Z / R Analytic Field = 250 Å } } Field = 1250 Å b s t BC = 100 b / t SWB = 100 s / t ~AR –3 ~AR –2
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Ti deposition into vias (which angular distribution?) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 020406080 (deg) Polar plot: cosine Subcosine (ellipse) * Ti at 2mTorr (Varian M2000) MC vapor transport code dN d — * suggested by Malaurie & Bessaudou (Thin Solid Films v. 286, 1996)
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Deposition StartEnd HRSEM Ti into vias cosine f( ) from gas transport code Experimental data Subcosine (ellipse) BC vs AR for several angular distributions Subcosine shows best agreement subcosine + scattering
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Full 3D — Across-wafer non-uniformity
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20cm wafer; 30cm target; depth = 0.8 m; AR = 2; deposited 0.4 m cut-away side view cut-away view from below Complex 3D features
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Off-axis circular via, depth = 0.85 m, aspect ratio, AR = 2.0, deposited 0.3 m z ( m) m yx Plan view x y Target wafer x off z LHS: Sees less of target RHS: Shadowed by overhang LHS Asymmetry in minimum step coverage ~ 10% Off-Axis Deposition
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More experimental validation — long-throw deposition (similar to ionized PVD)
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0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.00.51.01.52.02.53.0 w( ) (cm) Low pressure Ta PVD (circular via) Simulation takes angular distribution from vapor transport code Measured target erosion profile modelled by w( ) Z T = 10 cm R 3 cm P=1mTorr 1.0 0.0 dN — d 20406080 cosine
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Low pressure Ta PVD (circular via) Cosine (no erosion) Experimental Erosion + scattering Z T = 10 cm R 3 cm P = 1mTorr
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Columnar growth / roughness Z T = 10 cm R 3 cm P = 1mTorr Amplitude = 8 Amplitude = 4 m (400 X 400)
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Conclusions Level set code fast, accurate, predictive model for PVD of refractory metals Validated LS code using analytic formulae — Step coverage ~ AR –2 (trench) — Step coverage ~ AR –3 (via) LS code coupled to MC code through f( ) and “virtual” target Full 3D code Strong non-uniformity in coverage across wafer 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)
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