Computational Lithography Turning Physics into Yield

Transcription

1 Computational Lithography Turning Physics into Yield Tim Fühner Fraunhofer IISB Erlangen, Germany SEMICON Europa, TechArena,

2 Lithography Modeling 2 SEMICON Europa, TechArena,

3 Computational Lithography History 3 SEMICON Europa, TechArena,

4 Computational Lithography 4 SEMICON Europa, TechArena,

5 Outline Source Mask Optimization Models Imaging Photoresist Photomask Topography Pattern Multiplication EUV Directed Self Assembly Conclusions 5 SEMICON Europa, TechArena,

6 Lithography Exposure System 6 SEMICON Europa, TechArena,

7 Inverse Lithography Ill-posed reformulated as optimization problem 7 SEMICON Europa, TechArena,

8 Source/Mask Optimization (SMO) minimize target and pattern difference 8 SEMICON Europa, TechArena,

9 SMO Domain Coping with Ambiguity Directly Twostage 9 SEMICON Europa, TechArena,

10 SMO Representation parametric pixelated polar coordinates pixelated geometric archel-based mask representation 10 SEMICON Europa, TechArena,

11 SMO: Figures of Merit Pattern Fidelity Throughput Contrast Variation-insensitivity MEEF, Focus, Dose variations, PWs Constraints Manufacturability Design-related 11 SEMICON Europa, TechArena,

12 SMO Issues Resist Simulation Mask Topography Wafer Topography Multi-modal Search Spaces Process Performance Considerations 12 SEMICON Europa, TechArena,

13 SMO Example: DoF maximization defocus illumination mask none 200 nm 400 nm CD x : nm CD y : nm CD x : nm CD y : nm CD x : nm CD y : nm CD x : nm CD y : nm CD x : nm CD y : nm CD x : nm CD y : nm 13 SEMICON Europa, TechArena,

14 Source/Mask/Projector Optimization Generation 690 Generation Z4: 6 Z9: 4 Z16: 4 Z25: 98 Z36: 8 Z4: 6 Z9: 4 Z16: 13 Z25: 136 Z36: SEMICON Europa, TechArena,

15 Outline Source Mask Optimization Models Imaging Photoresist Photomask Topography Pattern Multiplication EUV Directed Self Assembly Conclusions 15 SEMICON Europa, TechArena,

16 Imaging Approaches Taxonomy Extremely fast Well suited for mask optimization Exact Well suited for Illumination optimization 16 SEMICON Europa, TechArena,

17 Resist Models First-principle resist models powerful and highly predictive But only, if adequately calibrated Multitude of parameters Measurements often hard to conduct No 1:1-correspondence between measured data and resist parameters And, they are also slow 17 SEMICON Europa, TechArena,

18 Simplified Resist Models: ADDIT Experiment: 13.8 mj/cm² 14.6 mj/cm² 15.4 mj/cm² 16.2 mj/cm² 17 mj/cm² 17.8 mj/cm² 18.6 mj/cm² 19.4 mj/cm² fast resist models lead to predictive results CD [µm] Simulation: 13.8 mj/cm² 14.6 mj/cm² 15.4 mj/cm² 16.2 mj/cm² 17 mj/cm² 17.8 mj/cm² 18.6 mj/cm² 19.4 mj/cm² less than 10 model parameters computation is up to 100 times faster than full resist models focus [µm] simulated and measured FEM Semi-dense lines (100 nm line 140 nm space) more details can be found in B. Tollkühn, SPIE 5376 (2004) SEMICON Europa, TechArena,

19 Simplified Resist Models Thresholdbased Constant Threshold Resist Model (CTRM) Variable Threshold Resist Model (VTRM) Diffused Aerial Image Model (DAIM) Acid Dose DIffusion Threshold (ADDIT) Model Lumped Parameter Models 2-D 3-D NG Approaches R3D RoadRunner 19 SEMICON Europa, TechArena,

20 Outline Source Mask Optimization Models Imaging Photoresist Photomask Topography Pattern Multiplication EUV Directed Self Assembly Conclusions 20 SEMICON Europa, TechArena,

21 Topographic vs. Kirchhoff Mask 21 SEMICON Europa, TechArena,

22 Rigorous EMF solvers FDTD Finite-difference time-domain Spatial domain Time-resolved RCWA Rigorous-coupled-wave-analysis, Waveguide Frequency domain Natural Transfer Matrix Extension Worse scaling behavior FEM Finite Element Method Complex geometries allowed Efficiency proportionate to mesh 22 SEMICON Europa, TechArena,

23 Hopkins Assumption multiple incidence angle onto mask angle difference increases with higher NA Hopkins assumption oblique incidences phase shift of spectrum fails 23 SEMICON Europa, TechArena,

24 Hopkins-Effects versus NA Annular, ypol, duty 1:1 NA=0.85 NA=0.9 NA=0.95 NA=1.0 Kirchhoff rig. Hopkins rig. without Hopkins NA=1.05 NA=1.1 NA=1.15 NA= SEMICON Europa, TechArena,

25 Field Decomposition Techniques Rigorous EMF simulations are required but too slow for Computational Lithography Speed-up techniques? horizontal configurations vertical configurations 1D configurations Y Axis Title = Y Axis Title Y Axis Title Y Axis Title X Axis Title X Axis Title X Axis Title X Axis Title Can be applied to both FDTD and Waveguide 25 SEMICON Europa, TechArena,

26 Field Decomposition Techniques 0 Performance of the Waveguide Decomposition Technique 0 accuracy for contact holes Y (nm) Image cross section 90nm dense contacts =193nm Intensity D Decomposition mask area 3.3 µm x 3.3 µm, typical EUV settings simulation times: X (nm) Full 3D: 400 days (estimation) Decomposition: 450 s Process window Parallelized Decomposition: 10s (27 CPU) 26 SEMICON Europa, 26 / TechArena, Intensity x (nm) Defocus (µm) 3D Decomposition

27 Mask Topography cont d 27 SEMICON Europa, TechArena,

28 EMF Approximations boundary layer pulse function filters e.g., Neural Network 28 SEMICON Europa, TechArena,

29 Comparison between models: CD Artificial Neural Network 29 SEMICON Europa, TechArena,

30 Outline Source Mask Optimization Models Imaging Photoresist Photomask Topography Pattern Multiplication EUV Directed Self Assembly Conclusions 30 SEMICON Europa, TechArena,

31 Crossed Lines: Wafer Topography Effects Impact of Refractive Index Difference Between Cured Resist 1 and Resist 2: n = n1 n2 resist footprints CD variation along resist 2 line p litho 1: 45nm lines; variable pitch litho 2: 45nm lines; 90nm pitch n = 0.03 Effect is linear in n/ k Material specifications have to be defined for critical pitches Consider critical pitch in the design split! 31 SEMICON Europa, TechArena,

32 Outline Source Mask Optimization Models Imaging Photoresist Photomask Topography Pattern Multiplication EUV Directed Self Assembly Conclusions 32 SEMICON Europa, TechArena,

33 EUV modeling topics Topographic masks shadowing, telecentricity Resist shot noise, secondary electrons y z x absorber capping Materials multi-layer, absorber MoSi multilayer OPC, RET 3-D OPC, phase-shift masks, assists substrate Mask defects assessment, repair 33 SEMICON Europa, TechArena,

34 Mesoscopic Resist Models and Line Edge Roughness How many photons do contribute to the exposure? for DUV exposures in an area of 1nm 1nm 2-5 for EUV exposures in an area of 1nm 1nm Continuous model Mesoscopic model general process performance, CD, process windows line edge roughness (LER) 34 SEMICON Europa, TechArena,

35 EUV-Mask Multilayer Defects: Geometry Typical defects bump defect pit defect (2D) Gaussian deformation at top/bottom h top/bot defect height w top/bot defect size (FWHM) 35 SEMICON Europa, TechArena,

36 Repair Simulation Results Impact of position and height h top =5nm aerial image footprint w top =50nm h bot =w bot =30nm performance versus height before repair after repair all defects up to a height of 6nm can be compensated 36 SEMICON Europa, TechArena,

37 EUV AttPSM: Contact Holes 22-nm square contact arrays, NA: 0.3, lambda: 13.5 nm Through-pitch (DR: 1:1, 1:2, 1:5) DOF: 100 nm Fixed 80 nm Cr Absorber: EL: 14.3%, PE: 2.81 nm 60 nm TaN (high k) absorber, 2% refl.: EL: 16.5%, PE: 2.1 nm 64 nm Mo (low k) absorber, 41% refl.: EL: 17.2%, PE: 2.13 nm 73 nm low n, high k absorber, 0.2% (bin.) refl.: EL: 17.2%, PE: 2.44 nm 37 SEMICON Europa, TechArena,

38 Outline Source Mask Optimization Models Imaging Photoresist Photomask Topography Pattern Multiplication EUV Directed Self Assembly Conclusions 38 SEMICON Europa, TechArena,

39 Directed Self-assembly (DSA) of block co-polymers Pattern Generation Pattern Rectification Graphoepitaxy Surface Brush Optical Lithography + DSA Modeling 39 SEMICON Europa, TechArena,

40 DSA Modeling Tasks and Requirements Pattern (equilibrium) prediction Impact of guiding structures Impact of litho process variations Pattern formation dynamics Defects, LER, LWR Impact of DSA process variations (i.e., anneal time/temperature) Material exploration and assessment 40 SEMICON Europa, TechArena,

41 DSA Modeling: Coarse Graining (images courtesy U. Welling, C. Daoulas, M. Müller, Georg-August-Universität Göttingen) 41 SEMICON Europa, TechArena,

42 Example: Single-Chain Mean-Field (SCMF) Approach Quasi-stationary particle-based approach Single chain: molecule level Chain interactions: molecular field Mean Field Di-block Co-Polymer (U. Welling, C. Daoulas, M. Müller, 10th Fraunhofer IISB Lithography Simulation Workshop, 2012) 42 SEMICON Europa, TechArena,

43 DSA simulation example Ordering kinetics in thin films : (PS-b- PMMA) on surface with stripe pattern L 0 = 47.5 nm Edwards, Stokovich, Müller, Solak, de Pablo, Nealey, J. Polym. Sci B 43, 3444 (2005) 43 SEMICON Europa, TechArena,

44 DSA Model Integration OPC-integrated SMO-integrated (cf. C. Liu, Proc. SPIE 8323, 2012) Compact models required 44 SEMICON Europa, TechArena,

45 Conclusions Lithography has a long-standing modeling and simulation history With narrowing margins, design has to increasingly consider the (litho) process Computational Lithography is all about bringing the achievements of Lithography Simulation to the design world But that s just the beginning 45 SEMICON Europa, TechArena,

46 Design Restrictions 46 SEMICON Europa, TechArena,

47 Conclusions Design centric world Process centric, ideal world 47 SEMICON Europa, TechArena,

48 Acknowledgements V. Singh (Intel), U. Welling (Uni Göttingen) All Fraunhofer IISB Lithography Simulation Group; esp. Andreas Erdmann, Viviana Agudelo, Peter Evanschitzky Simulations were performed with Dr.LiTHO ( THANK YOU FOR YOUR ATTENTION 48 SEMICON Europa, TechArena,