Fast simulation of nanoimprint lithography: modelling capillary pressures during resist deformation 20 October 2011 Hayden Taylor and Eehern Wong Simprint Nanotechnologies Ltd Bristol, United Kingdom Namil 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

Simulation can help select process parameters and refine designs in NIL 1 Taylor NNT 2009; 2 Taylor SPIE ; 3 Boning et al. NNT Pattern abstraction Density 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 ElastomerSilicon (nm) Time (s) Simulation size, N ~O(N 2 logN) 10 1 N

Chip-scale imprint simulation has until now addressed only thermal NIL Pa.s Resist viscosity during imprinting Externally applied pressure Capillary pressures Pa Thermal 4 UV 5 ThermalUV 4 e.g. Garcia-Romero, NNT 2008; 5 e.g. Auner, Organic Electronics 10 p Externally applied pressure Stamp Substrate Resist Pressure LowHigh 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 LowHigh Stamp Substrate Resist Capillary forces θ γ η Stamp Hydrophilic η θ = 90°

A simple modification to the simulation algorithm captures capillary effects r pgpg r pgpg r pgpg  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 γresist surface tension θresist-stamp contact angle sfeature pitch wcavity width ws

Contribution of capillary pressures diminishes with increasing feature size Silicon stamp Resist viscosity 50 mPa Surface tension 28 mN/m Contact angle 30° w

The new model has been tested experimentally 50 μm100 μm PDMS stamp E = 1.5 MPa; Thickness >> 150 μm Spun-on UVNIL resist Initial thickness: 85–165 nm; Viscosity: 30 mPa.s Silicon substrate Stamp much wider than pattern Parallel 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 ABCDE A B D

Simulation captures experimentally observed RLT variations Stamp Viscosity: 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 droplet Diameter > 10 μm 1.Reddy et al., Phys Fluids (2005) 2.Reddy and Bonnecaze, Microel. Eng (2005) 3.Morihara et al., Proc NNT Liang et al., Nanotechnology (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 mPa Surface tension 28 mN/m Contact 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 Resist model Chip design 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 Simulation uses  Viscosity/pressures Viscosity/pressures  Model capillary pressures Model capillary pressures  Integrate with model Integrate with model  Dependence on feature size Dependence on feature size  Experimental Experimental  Model vs expt Model vs expt  RLT homogenisation RLT homogenisation  Droplet demo Droplet demo