nanoimprint lithography modelling capillary pressures during resist deformation 20 October 2011 Hayden Taylor and Eehern Wong Simprint Nanotechnologies Ltd Bristol United Kingdom Namil ID: 307967
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Slide1
Fast simulation of nanoimprint lithography: modelling capillary pressures during resist deformation 20 October 2011 Hayden Taylor and Eehern WongSimprint Nanotechnologies LtdBristol, United KingdomNamil Koo, Jung Wuk Kim and Christian Moormann AMICA, AMO GmbH Aachen, Germany
TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A
hkt@simprintnanotech.com
+44 117 2302566Slide2
Simulation can help select process parameters and refine designs in NIL1 Taylor NNT 2009; 2 Taylor SPIE
7641 2010; 3 Boning et al. NNT 2010
0
0.51Pattern abstractionDensity
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 10
3
times faster than FEM
Can trade off spatial resolution and speed
92
99
10
165
Elastomer
Silicon
(nm)
Time
(s)
10
1
10
2
10
3
10
4
10
2
10
3
10
4
Simulation size,
N
~O(
N
2
log
N
)
10
1
NSlide3
Chip-scale imprint simulation has until now addressed only thermal NIL
10
-2
1102104106 Pa.s
Resist viscosity during imprinting
Externally applied pressure
Capillary pressures
10
10
3
10
5
10
7
10
9
Pa
Thermal
4
UV
5
Thermal
UV
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
forcesSlide4
ηHydrophobic
We incorporate capillary pressures into our fast NIL simulation algorithm
Need to know:
Resist viscosity, ηStamp-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°Slide5
A simple modification to the simulation algorithm captures capillary effectsr
pg
r
pg
r
p
g
No significant reduction in solution speed compared to thermal NIL simulation
Consider pressures acting on stamp in quasi-equilibrium:
p
capillary
(
x,y
) is pattern-dependent. Examples:
p
capillary
(
x,y
) falls to zero where cavities are filled
θ
γ
γ
resist surface tension
θ
resist-stamp contact angle
s
feature pitch
w
cavity width
w
sSlide6
Contribution of capillary pressures diminishes with increasing feature sizeSilicon stampResist viscosity 50 mPaSurface tension 28 mN/mContact angle 30° wSlide7
The new model has been tested experimentally50 μm100 μm
PDMS stampE = 1.5 MPa
;Thickness >> 150 μm
Spun-on UVNIL resistInitial thickness: 85–165 nm; Viscosity: 30 mPa.sSilicon substrateStamp much wider than patternParallel lines:
Protrusion width 85 nm
Out-of-page length ~ 2 mm
Protrusion height nom. 85 nm
Parallel lines:
Protrusion width 185 nm
Out-of-page length ~ 2 mm
Protrusion height nom. 85 nm
A
B
C
D
E
A
B
DSlide8
Simulation captures experimentally observed RLT variationsStampViscosity: 30 mPa.sSlide9
Fast capillary-driven filling is followed by residual layer homogenisation
Boning, Taylor et al. NNT 2010 Slide10
For droplet-based resist dispensing, a different approach is needed1 pL dropletDiameter > 10 μm Reddy et al., Phys Fluids 17 122104 (2005)Reddy and Bonnecaze, Microel. Eng. 82 60 (2005)Morihara et al., Proc NNT 2008Liang et al., Nanotechnology 18 025303 (2007)
Phenomena of interest: Speed of resist spreading 1
Likelihood of gas bubble entrapment
1-4Gas elimination after entrapment 4Slide11
Pressure distributions can be found for multiple droplets simultaneouslyResist viscosity 50 mPaSurface tension 28 mN/mContact angle 30° Resist thickness 200 nmWith zero external pressure:Stamp velocity = 56 nm/msSlide12
Summary and outlookCapillary pressures are added into our spin-on resist simulation algorithm Minimal increase in computation timeRLT homogenisation time is crucial for spun-on UVNIL processesA pressure algorithm is proposed for droplet-dispensed NILSimulation Engine
Physical
prediction
Resist
model
Chip
design
ProcessSlide13
AcknowledgementsMatthew DirckxTheodor Nielsen, Brian Bilenberg and Kristian Smistrup at NIL TechnologyDuane Boning, MITJames Freedman, MIT Technology Licensing OfficeMark BreezeSlide14
IndexSimulation usesViscosity/pressuresModel capillary pressuresIntegrate with modelDependence on feature sizeExperimentalModel vs exptRLT homogenisationDroplet demo