1. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 1 Copyright 2007, Regents of University of California Collaborative Platform, Tool-Kit, and Physical Models for DfM FLCC Pre-Presentation Feb 12 th, SPIE March 1, 2007 Andy Neureuther, Wojtek Poppe, Juliet Holwill, Eric Chin, Lynn Wang, Jae-Seok Yang, Marshal Miller, Dan Ceperley, Chris Clifford Jihong Choi, Dave Dornfeld UC Berkeley, Koji Kikuchi, Sony Visiting Industrial Fellow John Hoang, Jane Chang U.C. Los Angeles

2. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 2 Copyright 2007, Regents of University of California Need for Design for Manufacturability (DfM): Parametric Yield Loss Increasing to 25% Yield σ: ±5% Random Systematic Yield σ: +5% to -50% Source: IBS (RET impact) (leakage, performance, power) Discussion courtesy of Nickhil Jakatdar Leakage Power, Delay & Cross-Talk

3. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 3 Copyright 2007, Regents of University of California DfM Requirements: Context-based, Adaptive Model Resolution u In the Design l Increased model resolution for optimizing critical and sensitive paths u In the Flow l Adaptive model resolution and speed-accuracy trade- off to match abstraction level Nets/Paths Regions RTL Synthesis Prototyping Physical Synthesis Routing Optimization Sign-off Model Resolution Slide courtesy of Nickhil Jakatdar, Cadence

4. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 4 Copyright 2007, Regents of University of California Technology Advances to Integrate into DfM Slide courtesy Martin van den Brink, Photomask 06

5. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 5 Copyright 2007, Regents of University of California Opportunities in DfM u Platforms to Integrate Complexity l Broad Reach to encompass all contributions to complexity l New Collaborations (Process, Device, Design) supporting viewpoint of each with the intuitive terminology of each l New Functionalities for Visualization and Assessment u Physical Models l Very Fast and first-cut accurate l Complete set of processes: s Litho, CMP, Etch, Device, Metrology l Complete set of interactions between processes: s Litho-CMP

6. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 6 Copyright 2007, Regents of University of California Key Leverage Point for DfM: Express nonidealities at the mask plane u Move nonidealities to the mask plane for visualization and quantification early in Design

7. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 7 Copyright 2007, Regents of University of California Lithography Network: Tools and Concepts TEMPEST Collaborative Platform for DFM Pattern Matching Aberration Monitors IFT Pattern Matcher SPLAT Mask Layout Pattern (coma) Match Location(s) Aerial Image Simulator 90° 0° 180° 270° defocus

8. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 8 Copyright 2007, Regents of University of California Feature Level Compensation and Control: Industry and UC Discovery Collaboration 17 Supporters 2 Contributors 3 Former SVTC

9. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 9 Copyright 2007, Regents of University of California FLCC: Modeling and Characterization Litho Collaborative Experiments Toppan & Photronics Cypress & ASML AMD Device Metrology and Control CMP Etch CMP oxide poly-Si oxide

10. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 10 Copyright 2007, Regents of University of California Process Aware EDA Toolkit Interconnect Delay Lateral Image Effects Robustness Metrics Crosstalk Interactions & Placement Tool Kit Collaborative Platform for DfMDrag-and-Drop Hot Spot Fixer

11. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 11 Copyright 2007, Regents of University of California Collaborative Platform for DFM Collaborative Platform for DFM Collaborative Platform for DfM:Concept Circuit Simulation Transistor Modeling Process Simulation 65nm Testchips Simulation Experiment Parametric Yield Simulator Solutions across disciplines rather than within disciplines Wojtek Poppe

12. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 12 Copyright 2007, Regents of University of California Collaborative Platform for DfM: Implementation Module 1 Processing Module 3 Circuit Circuit Simulation across characterized process window Module 2 Device Non-rectangular transistors BSIM Model Standardized Input BSIM model Intuitive Parameters Viewpoint Rosetta Stone

13. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 13 Copyright 2007, Regents of University of California Collaborative Platform for DfM: Implementation Module 1 Processing Module 3 Circuit Circuit Simulation across characterized process window Module 2 Device Non-rectangular transistors BSIM Model Standardized Input BSIM model Intuitive Parameters Viewpoint Rosetta Stone SPLAT BSIM HSPICE

14. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 14 Copyright 2007, Regents of University of California Collaborative Platform for DfM: Use Processing Device Incremental Improvements Design Evaluation Circuits Create robustness metrics for process aware timing and power analysis CAD Implementation on Design Side

15. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 15 Copyright 2007, Regents of University of California Collaborative Platform for DfM: Validation FLCC Made possible by Feature Level Compensation and Control (FLCC) Enhanced Transistor Electrical CD Metrology Six contributors from five research areas

16. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 16 Copyright 2007, Regents of University of California Process Aware EDA Toolkit Juliet Holwill Lynn Wang Eric Chin Wojtek Poppe Interconnect Delay Lateral Image Interactions & Placement Robustness Metric Jae-Seok Yang Crosstalk quantifying the circuit performance robustness of layout snippits with an indexing metric control of leakage through maximizing optical image quality of drivers/buffers, mitigating optical spillover effects and optimizing robustness metrics in placement, visualizing chip level effects on delay variation, and checking robustness closure through estimating variations in interconnect.

17. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 17 Copyright 2007, Regents of University of California Drag-and-Drop Hot Spot Fixer Problem hotspot identified in layout on left. Drag and Drop Hotspot Fixer on right shows how a designer can drag a polygon with a mouse and have real-time hotspot re-evaluation as the polygon moves. Hotspot severity is color coded, green means hotspot free.

18. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 18 Copyright 2007, Regents of University of California Visualization of Focus Effects at Layout Juliet Holwill

19. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 19 Copyright 2007, Regents of University of California Image Behavior with Focus and Coma Cutline The intensity change with focus generally increases regardless of the sign, whereas the intensity of coma changes sign with the sign of coma.

20. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 20 Copyright 2007, Regents of University of California Pattern Matching Accuracy: Line End Shortening (LES) u LES can be modeled using the product of the match factor times the aberration level. u For Coma, LES is linear u For Defocus, LES is parabolic Juliet Make LES = Line End Shortening LES Coma Defocus LES Aberration Level

21. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 21 Copyright 2007, Regents of University of California Phase-Shifting Mask Spillover Trends Cutline The Pattern Match Factors and spillover light increases with the additional light through the mask but the impact on edge placement is partially mitigated by the increase in image slope. Mask Type SlopeComa MF ΔL Splat (0.02 Coma) ΔL PM (0.02 Coma) Focus MF ΔL Splat (0.04 Focus) ΔL PM (0.04 Focus) Binary6.950.0754nm0.30938nm Att. PSM 7.9330.0942nm0.31827nm Alt. PSM 9.0830.15018nm0.34230nm

22. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 22 Copyright 2007, Regents of University of California Mutual coherence functions taken from “Resolution Enhancement Techniques in Optical Lithography”, Alfred Wong. Annular IlluminationQuadrupole IlluminationDipole IlluminationTophat Illumination Off-Axis Illumination Trends: Mutual Coherence Mask Functions

23. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 23 Copyright 2007, Regents of University of California Polarization Trends Andy Make

24. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 24 Copyright 2007, Regents of University of California Assessment of Gate Layout Effects: Approach Juliet Make u The match factor is a measure of similarity of a layout geometry to a pattern at a particular location u It is calculated as the 2D discrete convolution u Range = [-1,+1] The pattern matcher is a fast tool for searching layouts for locations with the highest similarity to a given image. The input patterns are chosen to be the most sensitive to a particular aberration or illumination error. The snippets from these locations can then be simulated, rather than the whole layout

25. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 25 Copyright 2007, Regents of University of California Assessment of Gate Layout Effects: Results The match factors returned for a given layout may be used to predict the expected intensity change in the presence of coma Match Factor Intensity Change Match Factor Vs Intensity Change with Coma The match factors returned for a given layout may be used to predict the expected intensity change in the presence of coma Changing pitch and additional ‘kickers’ give this layout snippet a high match factor -0.100.25 -0.05 0.15

26. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 26 Copyright 2007, Regents of University of California DRC Compatible Monitors and Calibration Many modifications are possible, and each will be tested for sensitivity. These are some examples of constructed patterns that might be produced.

27. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 27 Copyright 2007, Regents of University of California Going where “Design Rules Do Not Reach” Function is about 5 feature sizes in diameter and easily reaches across cell or compaction boundaries. By computing influence functions for diffraction limited proximity Z1, Defocus Z4 and Coma Z7 it is possible to quickly assess image changes through the process window and along a scanner slit. Coma proximity effect

28. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 28 Copyright 2007, Regents of University of California Standard Cell Interactions: Approach Lynn Wang Cell iCell j Boundary MF = 0.3 Adjacent cells increase Match Factors and hence variation through focus

29. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 29 Copyright 2007, Regents of University of California Standard Cell Interactions: Accuracy

30. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 30 Copyright 2007, Regents of University of California Standard Cell Interactions: Results Greatest Range= 0.1 Smallest Range= 0.01 Cell 1 Cell 2 Cell 3 Cell 1 Cell 2 Cell 3 Cell 2 Cell 3

31. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 31 Copyright 2007, Regents of University of California CMP Variation Assessment: Physical Model Jihong Choi STI process simulation: HDPCVD and CMP HDPCVD CMP Dave Dornfeld HDPCVD CMP Characterization: Pad Asperity

32. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 32 Copyright 2007, Regents of University of California CMP Variation Assessment: Chip Model Pattern densityLine width Line space CMP model Chip Layout HDP-CVD Deposition Model CMP Input Thickness Evolution Nitride thinning

33. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 33 Copyright 2007, Regents of University of California Etch Variation Assessment: Physical Model John HoangJane Chang UCLA

34. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 34 Copyright 2007, Regents of University of California Etch Variation Assessment: Layout u Couple feature-profile simulations with tool-scale models and plasma ion energy models (Graves and Lieberman at UCB) u Identify factors in profile model that have feature level and pattern density dependencies Cl 2 N2N2 O2O2 WsWs W Coil Power Substrate Bias I outer I inner Pressure WsWs Chip Field LayoutEtcher

35. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 35 Copyright 2007, Regents of University of California Interconnect Variation Assessment: Concept Major Physical Contributors to Variation: - Lithography (Focus, Overlay, Aberrations, …) - CMP (Density, …) - Etch (Sidewall Angle, …) Key idea: Predict interconnect delay variations by tracing Pattern Matches through circuits and adjusting extracted RCs. Delay Variation = f(local layout, layout in layers above and below, die location, wafer position)

36. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 36 Copyright 2007, Regents of University of California Interconnect Variation Assessment: System Pattern Matcher Predict Geometrical Variations Estimate Changes in R, CNetlist Backannotation Timing Analysis Parasitic Extraction Fast-CAD Techniques: 1)Focus solely on critical paths to improve runtime 2)Pre-characterize libraries to model geometrical variations for different match factors.

37. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 37 Copyright 2007, Regents of University of California Interconnect Variation Assessment: Result Resistance Calculation: Values from ITRS 90nm Technology Node M1 GP 175nm ILD0 κ=3.4 450nm 110nm Nominal CMP Erosion Capacitance Extraction: Synopsys Raphael: 2D Field Solver Structure: Array Above Ground Plane 110nm

38. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 38 Copyright 2007, Regents of University of California Cross-Talks Variation Assessment: Concept AGGRESSOR VICTIM AGGRESSOR VICTIM AGGRESSOR Particle Driver variation - Non-linear rectangle poly(Litho) - Spatial correlation (Drivers are close) Interconnect variation -Width/Spacing(Litho, Etch) -Height(CMP) focus dose Target window Correlation factor (α) distance 1 Focus correlation Correlation factor(β) distance 1 Dose correlation + CD(Poly) - CD(poly) CD(Victim Driver) CD(Aggressor Driver) is bound focus dose Target window + CD - CD Crosstalk verification over the process window Jae-Seok Yang

39. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 39 Copyright 2007, Regents of University of California Cross-Talk Variation Assessment: Result Proposed flow for variation aware SI verification Layout (Full-chip) P & R OPC / Arerial image sim. RC extraction Xtalk analysis ED constraints considering spatial correlation for poly layer ED constraints considering spatial correlation for poly layer critical condidate nets in terms of Xtalk noise critical condidate nets in terms of Xtalk noise Real critical nets for DOF/dose margin Real critical nets for DOF/dose margin Arerial image sim. ( poly ) Arerial image sim. ( poly ) RC extraction Xtalk analysis ED constraints for metal Arerial image sim. ( metal ) Arerial image sim. ( metal ) Failure criterion Failure Repeat for the next ED condition Save as a critical net Yes Repeat for the next critical condidate nets Repeat for the next critical condidate nets No AGGRESSOR VICTIM AGGRESSOR Peak Noise over the process windows (mV) Defocus: -40nmDefocus: 0nmDefocus: 40nmDefocus: 100nm Dose: 10%243.1237.2238.3234.9 Dose: 0%242.3237.5234.4231.2 Dose: -10%234.8233.5232.7230.8 5.3% noise variation over the process window 65nm 100nm 100um Vdd=1.2V

40. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 40 Copyright 2007, Regents of University of California Photomask Edge Effect: Physics & Impact u Intensity imbalance IEDM 1992 Alfred Wong u Cherenkov radiation SPIE 2001 Costas Adam u Domain decomposition (edge sources) SPIE 2002 Costas Adam u PSM only 180 degrees for one pitch BACUS 2006 Gleason Form of Cherenkov radiation Air Glass Difference field = actual – vertical propagation field

41. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 41 Copyright 2007, Regents of University of California Photomask Edge Effect: Characterization u Both Real and Imy Edge Effects u Plot Sqrt(I) vs Duty Cycle u Adjusting Phase Etch Depth adds Purple to cancel Yellow Marshal Miller Koji Kikuchi Dan Ceperle y Real Imy C ER C EI

42. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 42 Copyright 2007, Regents of University of California Photomask Edge Effect: Characterization 5  deg 10 3 5

43. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 43 Copyright 2007, Regents of University of California Photomask Edge Effect: Characterization u Plot C ER and C ER vs mask period u Large periods give edge parameters u Small periods show cross-talk effects C ER C EI Period in TM TE Cross Talk

44. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 44 Copyright 2007, Regents of University of California Resulting Aerial Image EUV Defect/Mask Interactions: Modeling Chris Clifford Rapid Absorber Defect Interaction Computations for Advanced Lithography RADICAL: Mask with Buried Defect Defect

45. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 45 Copyright 2007, Regents of University of California EUV Defect/Mask Interactions: Assessment u Defect Projector for DFM l Characterize buried defect and mask pattern separately l Use fast methods to determine: s Effect of defect for various layouts s Layout mask blank interactions Defect Library Characterize Defect Ray Tracing Multilayer Simulator Possible Layouts and Defect Locations Severity of Pattern Variation Fast Interaction Simulator Based on RADICAL

46. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 46 Copyright 2007, Regents of University of California Conclusion: Nature of DfM u DfM requires l complexity management (beyond human comprehension) l new modes and multiple viewpoints for collaboration to integrate process, device and circuit l very fast first-cut accurate models l complete scope across processes and their interactions l circuit performance assessment (power, delay, cross-talk) u Designers add complexity management skills to process and device understanding and should be invited to collaborate. Thanks to DARPA, SRC, Industry and U.C. Discovery

47. FLCC Pre-Presentation of SPIE DFM-PI 07 ARN 47 Copyright 2007, Regents of University of California Conclusion: Strategies & Prototypes u Many nonidealities of manufacturing can be moved to the mask plane and visualized/quantified early. u Pattern Matching and Perturbation Modeling have both exceptional speed and adequate accuracy u Prototype DfM tools and methodologies were shown for l Parametric Yield Simulation process/device/circuits, l Visualization/quantification at the mask level Thanks to DARPA, SRC, Industry and U.C. Discovery