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)