Sub-65 nm CD Uniformity in Semiconductor Manufacture

1. Sub-65 nm CD Uniformity in Semiconductor Manufacture Terrence E. Zavecz tzavecz@TEAsystems.com

2. ITRS Roadmap Discussion Double Patterning & Overlay Implications Reticle Response Reticle signatures Variables included by Simulation Design for Manufacture (DFM) Problems CDU Signatures Process Behavioral Models Variance Budget The Approach to APC CDU Methods Discussion Agenda

3. ITRS 2005 Methods Lithography

4. Technology Requirements EUV Problem: Lens materials

5. Hyper NA immersion limits IMEC status paper 2006

6. Double Patterning for 32 nm half-pitch IMEC status paper 2006 • 1 nm overlay error = 1 nm CD change • Cross-talk failures from rotation

7. Poly DP Solution 193i – 1.2 NA X-Y Polarized light K1 = 0.28 Pitch 90 nm

8. Exposure Tool Solutions Double Patterning

9. Immersion n<=1 (H20) Immersion Hyper NA ( >1 to 1.45) Development Immersion Hyper NA ( >1 to 1.5) Double Patterning (32 nm node) EUVL 13.5 nm Immersion Driving the way @ 193 nm KrF Overlay 6 nm to 12 nm 4nm 12 nm

10. CD Metrology Technology

11. Overlay Technology

12. Reticle Response TEA Systems Yield Enhancement thru Modeling

13. Reticle Enhancement Techniques • “What you See” is not “What you get” • Design for Manufacture (DFM) Problems • Simulation • Neglect of most Process & Optic-stack signatures • Optical Proximity Correction • High-Frequency Limitations of Optical System • Inverse Lithography • Luminescent • Phase Shift Masks • Etch depth bias • Phase-Element Uniformity

14. Chrome Reticle 4:1 100 nm BCD Feature Width 1:2 Loading 1:1 Loading 1:3 Loading Chrome Oxide

15. What Effect on Wafer? MoSiON Fingerprint on Reticle Nanometrics Atlas M Scatterometry TEA Systems Weir PW Data Analysis

16. Photoresist & BCD due to Moly Signature Raw Photoresist Thickness Modeled BCD MoSiON Signature • Photoresist • 1:1 90 nm features • BCD Full-Field Symmetric Model • BCD component due to MoSiON • Piston & high-order removed • 4.5 nm Range Modeled BCD

17. Soft Metrology Errors • Target acquisition • Charging • Film-stack changes • BARC, Reflectivity etc • Recipe errors • Proximity • Nearest neighbor lying 2.5 * image size • Impact: • In-Line sampling • Process signature derivation • nm line, end-Gap metrology • Target acquisition error

18. Modeling Wafer Bias CDsem Reticle Wafer Scatter Reticle All Dimensions scaled to wafer final size

19. Residuals to Bias-Piston Note stable signature • Remove the Offset coefficient of every column • Plot the higher order bias.

20. -5.5 -5.9 -5.1 -5.0 -6.1 -7.1 -6.5 nm Wafer Bias, Scan Direction Changes Population Density (Boxplot) • Fixed-Focus Matrix • Modeled Field Row • Wafer bias • each slit position • Shown: center row End-of Scan Velocity change

21. Critical Feature UniformitySignatures TEA Systems Yield Enhancement thru Modeling

22. Motivation/Introduction • Sub-65 nm lithography CD budget approaches few nm... • active CD control is required to minimize CDU error budget components • which are the largest CDU error budget components? • can we correct CD errors in the same way we do for overlay? • Intra-wafer appears to be the largest CDU error component • Uncontrollable disturbances (process variations) are impossible to prevent or completely eliminate

23. Approach to intra-wafer CDU control • Process uncontrollable disturbances do not vary across the wafer in a completely random way • can be represented as stochastic processes with clear dependence on the spatial wafer coordinates • Disturbance dependence on intra-wafer spatial coordinates is determined by time-characteristics (how the wafer is processed in time) • whole wafer is processed at a time • variation tends to be a slow and continuous function • part of the wafer (I.e., single die) is processed at a time • variation is a discrete, non-continuous function

24. Problem Formalization • Relate process disturbance to a feature response • linear function with a constant coefficient describing the feature-disturbance sensitivity • feature response to disturbances with both continuous and discrete wafer spatial characteristics • model feature response components through high order polynomial fitting

25. Procedure - raw data analysis • Measured data in the form of: Raw MSE data MSE filter Field Average Wp (x,y) wafer periodic model r (x,y) wafer random error All filters All+REC filters DD(x,y) field model Slit & scan CD profiles

26. Signatures Outline • Experimental with feature response matching to process disturbances • Matching • characterization metrics • covariance

27. Experimental conditions • Exposures • typical 193nm litho process for 90nm features • AT1100 scanner, 0.75NA with annular illumination • 90nm gratings at 1:1; 1:2 and 1:3 with full field coverage • 240nm resist on 78nm Barc on Si • OCD metrology: NI, rotating polarized light (Nano9030) • diffractive optical metrology (scatterometry) - outputs spectral intensity changes of 0th order diffracted light intensity • modeled grating parameters • bottom CD; • resist thickness (Tr) and Sidewall Angle (SWA) • bottom arc thickness (Tbarc) • mean square errror (MSE)

28. OCD output parameters • Each fitted OCD feature response has a characteristics spatial distribution Bottom CD Tresist SideWall Angle Tbarc MSE

29. Process Behavioral Signatures • Process disturbances with characteristics spatial distributions and their feature response

30. Process Disturbances - feature response • Disturbance with slow and continuous spatial distribution • PEB plate to CD • Disturbance with discrete spatial distribution • programmed dose and defocus errors to CD PEB temp range of 1deg = CD variation range of 7nm PEB temp range of 0.5deg = CD variation range of 4nm

31. Tresist response to FEM SWA response to FEM Disturbance - feature response sensitivity • Experimental CD-sensitivity to process - expected • Experimental Tr and SWA sensitivity to exposure process

32. Std PEB + 4% PEB Temp - 4% PEB Temp -10% PEB time -0.1um defoc Best Focus -0.1um defoc Spatial Characteristics • CD spatial response to PEB variations • CD spatial response to defocus variations

33. Signatures Outline • Experimental with feature response matching to process disturbances • Matching • characterization metrics • covariance

34. Radial or XY intra-wafer modeled profiles • Wafers with PEB process disturbances (slow variation) • Radial plot inter-field model CD • XY plot inter-field model CD

35. CD N + 4%deg CD N -10%s CD N +10%s CD N - 4%deg Intra-wafer modeled profiles coefficients I • Wafers with PEB process disturbances • slow variation PEB Time PEB Temperature Fitted Profiles Triagonal Fitted Coefficients

36. Intra-wafer modeled profiles coefficients II • Wafers with Defocus process disturbances • slow variation Radial Profile statistics Fitted Linear Coefficients

37. Intra-wafer modeled profiles coefficients III • CD feature response to combined intra-wafer disturbances slow (PEB) and discrete (dose and defocus) Dose variation +/- 2% Focus variation +/- 0.05 um

38. Intra-wafer modeled profiles with coefficients III • 2-feature response to combined intra-wafer disturbances with slow (PEB) and discrete variation (defocus) • average field statistics for CD and SWA feature responses Focus variation +/- 0.05 um Dose variation +/- 2% um

39. Covariance between two feature responses to same process disturbance • Statistical values • Spatial characteristics Fitted Linear Coefficients of modeled intra-wafer profiles

40. UP DOWN 95.20nm 95.74nm Scan position Scan position UP-DOWN scan profiles • Scan direction: discrete intra-wafer disturbances • W2W scan disturbances - corrected for REC, wafer average

41. Summary: within wafer CDU components Main components - reticle - scanner - wafer process - residuals Within wafer CDU - barc thickness - PEB - develop - focus/dose - residuals Note: above data describes a specific process and scanner and could vary in a different environment

42. Effective Dose CDU Variance Signatures • Primary disturbances causing CD Uniformity (CDU) variations grouped upon their sources. Reticle Scanner Track & Process Substrate BARC Uniformity Transmission Wafer flatness Exposure Dose Uniformity Resist Film Uniform Slit Uniformity Device topography PEB Temp & Time Flatness Focus Stability Develop Feature CD Chuck Flatness Soft Metrology Error Up-Down Scan Scan Linearity Effective Focus CDU

43. BARC modeled wafer uniformity SPIE Vol. 5378-11

44. T3 (PR) SWA BARC BCD TCD Signatures across the wafer Variation after exposure

45. BARC Thickness & SWA But what happens when the process drifts? Normal process process wafer

46. - Scan Direction Artifacts Focus = -1.5 -1.0 -0.05 0.0 +0.05 +1.0 1.5 - + - + - + - + + - + - + - + Reticle Scan Direction

47. Profile variation with Focus SPIE: 5754-87 Lens Before cleaning Lens After cleaning Scan direction Top CD - + - + - + - - + - + - + - + - + - + - + + - + - + - + Slope Focus = -1.5 -1.0 -0.05 0.0 +0.05 +1.0 +1.5 Focus = -1.5 -1.0 -0.05 0.0 +0.05 +1.0 +1.5 Bottom CD Process window

48. 5 18 19 6 9 10 11 12 Slope vs Dose across the slit Lens Before cleaning Lens After cleaning Up +Scan Down -Scan

49. Signatures Summary • Litho-process intra-wafer CDU characteristics were analyzed as feature-responses to process disturbances • analysis done as statistical values and spatial distributions • Statistical values • After removing reticle contribution, the largest CDU error component appears to come from the process - PEB and Tbarc • Information from Spatial Patterns identifies common spatial dependencies to CDU error sources (process disturbance) • Process components are characterized by ‘low-frequency’ variation Could be modeled by 6th degree XY polynomial fitting • possible metrics: fitted profile, fitted coefficients • Scanner components are randomly distributed with localized variations. These effects are more difficult to be extracted due to interference with process variations • scan UP and DOWN and W2W intrafield fingerprints do NOT contribute to this CDU error budget!

50. Approach to APC