Contamination Transport from Wafer to Lens

Transcription

1 Contamination Transport from Wafer to Lens Immersion Lithography Symposium August 4, 2004 Greg Nellis, Roxann Engelstad, Edward Lovell, Alex Wei, Mohamed El-Morsi Computational Mechanics Center, University of Wisconsin, Madison, WI Chris Van Peski International SEMATECH Research supported by International SEMATECH and DARPA/ARL Computational Mechanics Center Slide 1

2 Lens Contamination by Resist Material Some resist material may migrate to lens Most resist material is carried away with fluid Dispense Lens Recover Chuck Soluble resist material is released into immersion fluid Device Wafer Computational Mechanics Center Slide 2

3 Presentation Outline Immersion Symposium Objective: Preliminary assessment of the potential impact of resist leaching and fluid contamination on lens lifetime. Contamination transport model Estimating model parameters Parallel flow simulation results: Aligned flow Stationary wafer Opposed flow Fluid cleanliness Recirculating flow simulations Computational Mechanics Center Slide 3

4 Mass Transfer Governing Equation (2-D) 2 2 c c c c f u + f v = f D + 2 f D 2 ρ ρ ρ ρ x y y x mass transport by bulk motion mass transport by diffusion c is the local concentration of the contaminant D is the diffusion coefficient u and v are the velocity components in the x- and y- directions Computational Mechanics Center Slide 4

5 Contamination Transport Model Material is transported against concentration gradients by diffusion (D) Material is transported in direction of velocity by bulk fluid motion L L f = u w τ h Chuck Soluble resist material is released into immersion fluid (m, τ) Velocity distribution ( P, u w, µ, ρ) Computational Mechanics Center Slide 5

6 Amount of Contaminants Released (m ) Hinsberg et al. 1 measured ppm of contaminants in a 0.5 ml droplet placed on a resist layer for 2 min. based on the droplet area and contaminant mass: m = 1000 ng/cm 2 Other estimates at ISMT place upper bound of contaminants at 60 mg/cm 3 through a 150 nm resist layer complete depletion would result in: m = 900 ng/cm 2 Reasonable upper bound on m is 1000 ng/cm 2 1. W. Hinsberg et al., Liquid Immersion Lithography for 193 nm: Resist-Liquid Interactions Status and Update, presented at the 2 nd Immersion Lithography Workshop, IBM Almaden, July 11, Computational Mechanics Center Slide 6

7 Time Required to Release Contaminant (τ) Time required for a wafer to move across a 4.0 cm lens at 0.5 m/s is less than 100 ms. Hinsberg et al. 1 measured contaminant concentration after 2 minutes and reported that release was essentially complete As τ becomes much less than 100 ms, the results become insensitive to τ Reasonable lower bound on τ is 10 ms Computational Mechanics Center Slide 7

8 Diffusion Coefficient (D) Immersion Symposium Reasonable upper bound on D is 1e-8 m 2 /s Computational Mechanics Center Slide 8

9 Simulation Parameters Immersion Symposium Wafer velocity Parameter Symbol Nominal Value Reasonable Range u w Fluid viscosity µ Driving pressure difference Gap height Gap length Fluid density Diffusion coefficient P Material released m Time required for release h L ρ D τ 0.5 m/s -0.5 to 0.5 m/s Pa-s 200 Pa* 1.0 mm 4.0 cm 997 kg/m 3 1e-8 m 2 /s 1000 ng/cm 2 10 ms - 0 to 2000 Pa e-8 to 1e-9 m 2 /s 10 to 1000 ng/cm 2 10 to 1000 ms * note that 200 Pa corresponds to a bulk velocity of 0.5 m/s with no wafer motion or a flow rate of nominally 500 ml/min Computational Mechanics Center Slide 9

10 Contamination Deposited on Lens ( ) & = ρ c mlens x f D y y= h y x average mass flux on lens: maximum mass flux on lens: L c m& lens = ρ f D dx y 0 y= h ( ) m& lens, max = MAX m& lens x from 0 to L Computational Mechanics Center Slide 10

11 Aligned Flow Simulation Immersion Symposium Uncontaminated liquid enters: c x=0 = 0 Lens is clean : c y=h = 0 Chuck Mass flux over release zone: c m ρ D = y No mass flux over depleted wafer: c ρ D = 0 y f f τ y= 0 y= 0 Computational Mechanics Center Slide 11

12 Aligned Flow Simulation at Nominal Conditions concentration very thin layer of contamination confined to the wafer surface maximum flux on lens and average flux on lens are small over its entire length Computational Mechanics Center Slide 12

13 Parametric Study Aligned Flow m & as P lens, max as u w m & lens, max note 10 Pa corresponds to ~ m/s or 25 ml/min Computational Mechanics Center Slide 13

14 Impact of Contamination Buildup Primary effect: loss of transmission through optical system The mass per area (m ) required to affect a given loss of transmission (floss) is approximately: m = 0.14MW floss 3 r NA α MW = molecular weight r = size α = absorption coefficient N A = Avogadro s number Computational Mechanics Center Slide 14

15 Impact of Contamination Buildup The simulation results can be used to predict the allowable operating time (optime) assuming that all flux reaching the lens eventually sticks to it: MW floss optime = r N α m& A convenient way of presenting the simulation results is as a Figure of Merit: Figure of Merit = A 3 optimer α 0.14 = MW floss N m& A Computational Mechanics Center Slide 15

16 Reduced Operating Time Immersion Symposium acceptable Example: r = 10 Angstrom MW = 134 kg/kgmol, α = 10 micron -1 floss = 0.05 optime = 1 year Required Figure of Merit = 5e-14 kgmol-m 2 -s/kg unacceptable Computational Mechanics Center Slide 16

17 Stationary Wafer Simulations wafer stops suddenly mass flux of contamination persists for some time how much driving pressure is required to prevent it from reaching lens? Computational Mechanics Center Slide 17

18 Stationary Wafer Simulations ~ 20 Pa required (0.05 m/s bulk velocity 50 ml/min) acceptable unacceptable Computational Mechanics Center Slide 18

19 Opposed Configuration Immersion Symposium Dispense Recover y r Chuck ( 0, ) c x= y > y = r y r 0 ( ) ( = 0, < r ) h c( x L y yr ) v( y) dy v y c x y y dy y r = < = h y, r ( ) ( =, > ) v y c x L y y dy h y r ( ) v y dy r Computational Mechanics Center Slide 19

20 Opposed Flow Results at Nominal Conditions concentration thicker layer of contamination concentration boundary layer near lens Computational Mechanics Center Slide 20

21 Parametric Study Opposed Flow m & as P lens as u w m & lens Note that 2000 Pa corresponds to approximately 5 m/s or 5000 ml/min Computational Mechanics Center Slide 21

22 Parametric Study Aligned & Opposed Flow aligned flow opposed flow Computational Mechanics Center Slide 22

23 Opposed Flow Reduced Operating Time Computational Mechanics Center Slide 23

24 Opposed Flow Effect of m and τ nominal conditions used for model Immersion Symposium Computational Mechanics Center Slide 24

25 Effect of Fluid Cleanliness Immersion Symposium Contaminated liquid enters: c x=0 = c fluid Lens is clean : cy=h = 0 Chuck No mass flux from wafer: c D y = Computational Mechanics Center Slide 25 y 0 = 0

26 Effect of Fluid Contamination Immersion Symposium Computational Mechanics Center Slide 26

27 Effect of Fluid Contamination Immersion Symposium acceptable unacceptable Example: r = 2 Angstrom MW = 50 kg/kgmol, α = 5 micron -1 floss = 0.10 optime = 1 year Figure of Merit = 2.5e-16 kgmol-m 2 -s/kg Computational Mechanics Center Slide 27

28 Recirculating Flow Experimental Evidence 2.0 mm Gap m/s jet Dispense port Lens view from below Computational Mechanics Center Slide 28

29 Recirculating Flow Immersion Symposium recover dispense recover lens recirculating region on recover-side of dispense port may shield lens recirculating region on lens-side of dispense port may contaminate lens Computational Mechanics Center Slide 29

30 Recirculating Flow Simulation Immersion Symposium 10.0 mm 0.5 mm x 0 Pa 0 Pa 0.6 m/s 1.0 mm L flux L flux can be changed either through τ or u w Computational Mechanics Center Slide 30

31 Recirculating Flow Simulation Immersion Symposium housing can be designed to provide protection for lens given u w and τ Computational Mechanics Center Slide 31

32 Summary Immersion Symposium Characteristics of the contamination released into immersion fluid has been bounded: 1000 ng/cm 2 of contamination 10 ms release time 1e-8 m 2 /s diffusion coefficient Simulations of contamination transport were developed to predict mass flux on lens (average and maximum) Mass flux used to predict a Figure of Merit: 3 optimer α MW floss Computational Mechanics Center Slide 32

33 Summary Immersion Symposium Aligned flow simulations show that small velocities or driving pressures keep contamination at acceptable levels Opposed flow simulations show that large driving pressures may be required to control contamination due to recirculation Simulations show that fluid must be very clean to prevent significant accumulation Recirculating regions will likely exist in a practical dispense/recover system regions prior to the dispense may protect it regions beyond the dispense will accelerate contamination Computational Mechanics Center Slide 33