Post on 17-Jan-2016
transcript
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
Background
Multi-level interconnects / metallization for ICs
Tungsten (W) deposited incircular “vias” (plugs) usingCVD
Al lines (Cu electro-deposited in long trenches)
Thin Films for Metalization
Cu TaSiO2
Si
• WF6 + 3H2O W + 3O + 6 HF etches SiO2
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
2m
Simulation of PVD into trench
Low bottomcoverage
Keyhole formation
Low side-wall coverage
Barrier failure
• Metallic films are polycrystalline
Micro-voids and grain boundaries
Columnar (rough) growth and pores more likely because of oblique incidence & lowsurface diffusivity
10nm
impinging atoms
~ 0.25m
( Monte Carlo simulations by Jacques Dalla Torre & George Gilmer )
Objectives: 1. Predict film coverage across wafer 2. Optimize deposition process
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• Problems, challenges
• Numerics for moving interface:• Level sets
Low pressure PVD—DC magnetron sputtering
Rotating magnetic field “traps” electrons => non-uniform target erosion
sputter target
Ti, Ta, Al, Cu, ....
+V
S N SN
wafer
-V
Ar+
ArP ~ 1 - 20 mTorr
+V
plasma
30 cm
Target
Feature on wafer
Sputter
L Rn
• 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 usinglevel sets
• 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
Continuum Modeling
Low pressure PVD — Monte Carlo vapor transport code
S N SN
wafer
sputter target
Rotating magnetic field “traps” electrons
-V
Ar+
Ar
Ti, Ta, Al, Cu, ....
P ~ 1 - 20 mTorr
+V
plasma +VBinary collision MC code gives resultant angular distribution, f(), just above wafer
f() then used in level set code
“virtual” target
Computation of geometric 3D material flux
0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40 50 60 70 80 90
(deg)
3D MD data for Al
Nonlinear curve fit
Equivalent 2D flux
Cos
f(
A
r
discrete surfaceelement on target
discrete surfaceelement on substrate
n
Deposition rate given by:
w() f() cos r 2dA
visibleregion
F3D(substrate) =
w() = weight function from target erosion profile
f(cos((isotropic emission from target)
f(
f(
cosA kk
k ......from molecular dynamics calculations
Can use differentangular distibutions:
......Monte Carlo vapor transport code
Code / model validation
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
Zwafer
Step coverage vs. AR : Circular Via
Side-wall coverage
Analytic
Bottom coverage
22
AR41Q1
100)BC(
0t
AR = h / wQ = Z / R
Analytic
Field = 250 Å }
} Field = 1250 Å
bs
t
BC = 100 b / tSWB = 100 s / t
~AR–3
~AR–2
Ti deposition into vias (which angular distribution?)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 20 40 60 80 (deg)
Polar plot:cosine
Subcosine (ellipse) *
Ti at 2mTorr (Varian M2000)MC vapor transport code
dNd—
* suggested by Malaurie & Bessaudou (Thin Solid Films v. 286, 1996)
Deposition
Start End
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
Full 3D — Across-wafer non-uniformity
20cm wafer; 30cm target; depth = 0.8m; AR = 2;deposited 0.4m
cut-away side view
cut-away viewfrom below
Complex 3D features
Off-axis circular via, depth = 0.85m, aspect ratio, AR = 2.0,deposited 0.3m
z (
m)
m
yx
Plan view
x
y
Target
wafer
xoff
z
LHS: Sees less of target
RHS: Shadowed by overhang
LHS
Asymmetry in minimum step coverage ~ 10%
Off-Axis Deposition
More experimental validation — long-throw deposition (similar to ionized PVD)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0.0 0.5 1.0 1.5 2.0 2.5 3.0
w()
(cm)
Low pressure Ta PVD (circular via)
• Simulation takes angular distribution from vapor transport code
• Measured target erosion profile modelled by w()
ZT = 10 cm
R 3 cm
P=1mTorr
1.0
0.0
dN —d
20 40 60 80
cosine
Low pressure Ta PVD (circular via)
Cosine (no erosion) Experimental Erosion + scattering
ZT = 10 cm
R 3 cm
P = 1mTorr
Columnar growth / roughness
ZT = 10 cm
R 3 cm
P = 1mTorr
Amplitude = 8 Amplitude = 4
m (400 X 400)
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)