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Transcript of Fast simulation of nanoimprint lithography: modelling capillary pressures during resist deformation...
Fast simulation of nanoimprint lithography: modelling capillary pressures during resist deformation
20 October 2011
Hayden Taylor and Eehern WongSimprint Nanotechnologies LtdBristol, United Kingdom
Namil Koo, Jung Wuk Kim and Christian Moormann AMICA, AMO GmbH Aachen, Germany
[email protected]+44 117 2302566
Simulation can help select process parameters and refine designs in NIL
1 Taylor NNT 2009; 2 Taylor SPIE 7641 2010; 3 Boning et al. NNT 2010
0
0.5
1Pattern abstraction
Den
sity
Resist surface’s impulse response
Resist Substrate
Stamp’s load response (bending, indentation)
Resist
Stamp
Example questions:
Does changing stamp material affect
residual layer uniformity? 1,2
Can ‘dummy fill’ accelerate stamp cavity filling? 3
Simulations need to be highly scalable
At least 103 times faster than FEM
Can trade off spatial resolution and speed
92
99
10
165
Elastomer Silicon
(nm)
Tim
e (s
)
101 102 103 104
102
103
104
Simulation size, N
~O(N2logN)
101
N
Chip-scale imprint simulation has until now addressed only thermal NIL
10-2 1 102 104 106 Pa.s
Resist viscosity during imprinting
Externally applied pressure
Capillary pressures
10 103 105 107 109 Pa
Thermal 4UV 5
ThermalUV
4 e.g. Garcia-Romero, NNT 2008; 5 e.g. Auner, Organic Electronics 10 p.1466 2009
Externally applied
pressure
Stamp
Substrate
Resist
Pressure
Low High
Capillary forces
η
Hydrophobic
We incorporate capillary pressures into our fast NIL simulation algorithm
Need to know: Resist viscosity, η Stamp-resist contact angle, θ Resist’s surface tension, γ
Externally applied
pressure
Pressure
Low High
Stamp
Substrate
Resist
Capillary forces
θ
γ
η
Stamp
Hydrophilic
η
θ = 90°
A simple modification to the simulation algorithm captures capillary effects
r
pg
r
pg
r
pg
No significant reduction in solution speed compared to thermal NIL simulation
Consider pressures acting on stamp in quasi-equilibrium:
pcapillary(x,y) is pattern-dependent. Examples:
pcapillary(x,y) falls to zero where cavities are filled
s
cos2
cos1
2
s
w
s
2
cos4
s
w
θ
γ
γ resist surface tensionθ resist-stamp contact angles feature pitch w cavity width
w s
Contribution of capillary pressures diminishes with increasing feature size
w
The new model has been tested experimentally
50 μm 100 μm
PDMS stampE = 1.5 MPa;Thickness >> 150 μm
Spun-on UVNIL resistInitial thickness: 85–165 nm; Viscosity: 30 mPa.s
Silicon substrate
Stamp much wider than pattern
Parallel lines:Protrusion width 85 nmOut-of-page length ~ 2 mmProtrusion height nom. 85 nm
Parallel lines:Protrusion width 185 nmOut-of-page length ~ 2 mmProtrusion height nom. 85 nm
A B C D E
A B D
Simulation captures experimentally observed RLT variations
StampViscosity: 30 mPa.s
Fast capillary-driven filling is followed by residual layer homogenisation
Boning, Taylor et al. NNT 2010
For droplet-based resist dispensing, a different approach is needed
1 pL dropletDiameter > 10 μm
1. Reddy et al., Phys Fluids 17 122104 (2005)2. Reddy and Bonnecaze, Microel. Eng. 82 60 (2005)3. Morihara et al., Proc NNT 20084. Liang et al., Nanotechnology 18 025303 (2007)
Phenomena of interest: Speed of resist spreading 1
Likelihood of gas bubble entrapment 1-4
Gas elimination after entrapment 4
Pressure distributions can be found for multiple droplets simultaneously
Resist viscosity 50 mPaSurface tension 28 mN/mContact angle 30° Resist thickness 200 nm
With zero external pressure:Stamp velocity = 56 nm/ms
Summary and outlook
Capillary pressures are added into our spin-on resist simulation algorithm
Minimal increase in computation time
RLT homogenisation time is crucial for spun-on UVNIL processes
A pressure algorithm is proposed for droplet-dispensed NIL
Simulation Engine
Physical prediction
Resist model
Chip design
Process
Acknowledgements
Matthew Dirckx Theodor Nielsen, Brian Bilenberg and Kristian
Smistrup at NIL Technology Duane Boning, MIT James Freedman, MIT Technology Licensing Office Mark Breeze
Index
Simulation uses Viscosity/pressures Model capillary pressures Integrate with model Dependence on feature size Experimental Model vs expt RLT homogenisation Droplet demo