Feature-level Compensation & Control

1. FLCC Feature-level Compensation & Control CMP April 5, 2006 A UC Discovery Project

2. Chemical Mechanical Planarization - Faculty Team David A. Dornfeld Mechanical Engineering UCB Mechanical Phenomena Fiona M. Doyle Materials Science and Engineering UCB Chemical Phenomena Interfacial and Colloid Phenomena Jan B. Talbot Chemical Engineering UCSB FLCC - CMP

3. Chemical Mechanical Planarization - Student Team Sunghoon Lee ME-UCB Jihong Choi ME-UCB Mechanical Phenomena Alex DeFeo ME-UCB Diego Arbelaez ME-UCB Summer ‘06 Chemical Phenomena Interfacial and Colloid Phenomena Robin Ihnfeldt Chem Eng UCSB Shantanu Tripathi ME/MSE UCB FLCC - CMP

4. CMP Research Description: The major objective of this work continues to be to establish an effective linkage between capable process models for CMP and its consumables to be applied to process recipe generation and process optimization and linked to device design and other critical processes surrounding CMP.Specificissues include dishing, erosion and overpolishing in metal polishing, which have an impact on circuit performance — all pattern dependent effects at the chip level — wetability effects in polishing, and novel consumable design (pads and abrasives) for optimized performance. We develop integrated feature-level process models which drive process optimization to minimize feature, chip and wafer-level defects. Goals: The final goals remain reliable, verifiable process control in the face of decreasing feature sizes, more complex patterns and more challenging materials, including heterogeneous structures and process models linked to CAD tools for realizing “CMP compatible chip design.” FLCC - CMP

5. Current Milestones • Wetting studies on two phase or multiphase surfaces (CMP Y3.1) Studies on modification of the wetting behavior through optimized use of surfactants and other solvents. • Further development of basic understanding of agglomeration/dispersion effects (CMP Y3.2) Experimental analysis of slurry particle size characteristics. Study influence of chemistry on particle behavior for characterizing particle size effects. • SMART pad design scaleup and validation (Y3.3) Scaleup (larger size) and validation of SMART pad design for more commercially viable experimental conditions for enhanced planarization with reduced overpolishing (ILD) and dishing and erosion (metal). • CMP process model development (Y3.4) Continue development of model for characterizing chip scale pattern dependencies for process optimization with respect to within die and within wafer nonuniformity; validate with specific tests patterns; formulation as “CMP compatible chip design” software. • Mechanisms for coupling of chemical and mechanical phenomena in CMP (CMP Y3.5) Develop chemical models to characterize the material removal due to chemical/electrochemical effects, and integrate the chemical models into the comprehensive CMP model to account for mechanical, interfacial and chemical phenomena. • Basic material removal model development (Milestone continued from Y2, CMP Y3.6) Continue development of process model and validation with attention to low down force applications/ non-Prestonian material removal as well as subsurface damage effects; applicable to electrolytic polishing (E-CMP) as well. FLCC - CMP

6. Student Research and Milestones Shantanu Tripathi • Wetting studies on two phase or multiphase surfaces (CMP Y3.1) • Further development of basic understanding of agglomeration/dispersion effects (CMP Y3.2) • SMART pad design scaleup and validation (Y3.3) • CMP process model development (Y3.4) • Mechanisms for coupling of chemical and mechanical phenomena in CMP (CMP Y3.5) • Basic material removal model development (Milestone continued from Y2, CMP Y3.6) Robin Ihnfeldt Sunghoon Lee Alex DeFeo Jihong Choi Shantanu Tripathi Shantanu Tripathi Diego Arbelaez FLCC - CMP

7. Today’s Presentation- see the posters for details - SMART Pad (Pad Micro Feature Design for CMP with Sensor Integration) • Pad prototypes • Validation results • Sensors for CMP/ Sensor requirements (proposed research) Integrated Tribo-Chemical CMP Model • Mechanisms for coupling of chemical and mechanical phenomena in CMP • Basic material removal model development Colloidal behavior of slurry particles • agglomeration/dispersion effects on CMP • Incorporate colloidal and chemical effects into basic material removal model Chip Scale Modeling of High Selectivity STI CMP, Linking HDP-CVD Oxide Topography • Application of chip scale model to high selectivity STI process • Modeling HDP-CVD oxide topography for CMP model input • Model calibration by comparison with test pattern wafer CMP results • Model application and verification for production pattern wafer FLCC - CMP

8. Colloidal behavior of slurry particles • Develop basic understanding of agglomeration/dispersion effects on CMP • Characterize slurry colloidal behavior using zeta potential and particle size measurements • Understand effects of common chemical additives and presence of copper nanoparticles • Study particle size distribution – Gaussian or bimodal? • Investigate the effects of slurry chemistry on copper surface hardness • Determine the state of the Cu (Cu, CuO, Cu2+, etc.) both in the slurry and on the wafer surface • Basic material removal model development • Incorporate colloidal and chemical effects into the Luo-Dornfeld model (IEEE Trans. on Semi. Manuf. 2001) thru: • particle size and distributions • copper surface hardness FLCC - CMP

9. Zeta Potential and Particle Size a) Zeta potential and b) particle size versus pH for alumina in a 1 mM KNO3 solution with and without 0.12 mM copper (error bars indicate standard deviation of particle size distribution). • IEP = ~6.5 with and without copper • Agglomerates larger with copper at pH >7 FLCC - CMP

10. Potential-pH Diagram for Cu-H2O Potential-pH diagram for the copper-water system with [Cu]=10-4 M at 25°C and 1 atm (M. Pourbaix 1957) FLCC - CMP

11. Nanohardness Measurements of 1mm Cu on 15 nm Ta on silicon after 10 min exposure to 5 wt% H2O2 slurry solution Nanomechanical Test Instrument from Hysitron, Inc. *A. Jindal and S. V. Babu, J. Electrochemical Society, 151 10 (2004). FLCC - CMP

12. Model MRR Predictions alumina slurry containing 0.1M Glycine and 2.0 wt% H2O2 Modified Lou and Dornfeld model (with full particle size distribution in presence of copper particles and measured hardness) Lou and Dornfeld model • MRR predictions improved at low pH values. • MRR predictions are highly sensitive to the standard deviation of theparticle size. FLCC - CMP

13. Conclusions Chemical Additives • pH has the greatest effect on colloidal behavior • Glycine acts as a stabilizing agent for alumina • Addition of both glycine and H2O2 increases MRR • Agglomerate size distribution becomes broader as zeta potential becomes smaller and particles agglomerate Presence of Cu nano-particles • Larger alumina agglomerates at pH>7 • Magnitude of zeta potential is slightly lower Luo and Dornfeld model • MRR predictions improved using distributions in presence of copper and measured surface hardness FLCC - CMP

14. Future Goals -Further develop understanding of chemical/colloidal effects • Continue to investigate the effects of additives on the copper surface hardness • Study colloidal properties of nanoparticles in the slurries • Determine the state of the copper (Cu, CuO, etc.) in solution as well as on the wafer surface -Basic material removal rate model development • Investigate the model sensitivity to changes in particle size and standard deviation • Use CMP data to develop chemical component of FLCC developed models (Luo, Choi) FLCC - CMP

15. Integrated Tribo-Chemical CMP Model Design & simulation of CMP process Lower pressure copper CMP for porous low-K. With technology node moving 65nm & below impact of defects much greater. CMP process underutilized – possibility of getting higher performance (higher removal rate, higher planarity, lesser defects, lesser pressure) at lower costs. Need for fundamental understanding, need for bringing together different segments of CMP knowledge FLCC - CMP

16. 2006 Main Objective • Mechanisms for coupling of chemical and mechanical phenomena in CMP (CMP Y3.5): • Develop chemical models to characterize the material removal due to chemical/electrochemical effects, and integrate the chemical models into the comprehensive CMP model to account for mechanical, interfacial and chemical phenomena. • Basic material removal model development (Milestone continued from Y2 (CMP Y3.6)) • Development of process model and validation with attention to low down-force applications/non-Prestonian material removal, as well as subsurface damage effects; applicable to electrolytic polishing (E-CMP) as well. FLCC - CMP

17. The Problem Interactions: • Asperity-copper • Abrasive-copper Needed: an Integrated Copper CMP Model Pad Pressure/ Velocity Abrasive Oxidizer Inhibitor Complexing agent Surface Film Integrated Cu CMP Model Colloid Agglomeration Fluid Mechanics Mass Transfer Needed: understanding of the synergy between different components Fluid pressure Contact pressure FLCC - CMP

18. Challenges Chemical behavior complex – highly sensitive to chemistry, which can change during CMP Mechanical material removal more pronounced Evidence of synergy between chemical and mechanical effects Knowledge Gaps Asperity-copper interaction • Local pressure vs material removal • Threshold pressure and removal saturation • Dissolution vs surface film growth • Interval between consequent asperity-wafer contacts Abrasive-copper interaction • Soft surface film removal: • Plowing • Plucking • Mechanically enhanced chemical reaction FLCC - CMP

19. Dornfeld – Luo Model Framework connecting input physical parameters with material removal rate FLCC - CMP

20. Tribological Models Contact Model: • Indentation depth & MRR applied pressure • Real contact area  applied pressure • Properties of surface film (thickness, effective hardness) • Material removal per abrasive • #abrasives per asperity • Overall removal rate Sundararajan, Thakurta, Gill, J. Electrochem. Soc., 146, 761-766 (1999) • Hydrodynamic Model • No pad-wafer contact • Thin slurry film (h < 20–50 µm) • However, Danyluk et al claim interfacial fluid pressure is sub-ambient. FLCC - CMP

21. Electrochemical Behavior of Copper Pourbaix diagram: Copper-glycine system S. Aksu & F. M. Doyle, J. Electrochem. Soc., 148, B51-B57 (2001) Copper CMP model: method of kinetics E. Paul, F. Kaufman, et al. J. Electrochem. Soc., 152, G322-G328 (2005) FLCC - CMP

22. Passivation in H2O2 Active behavior Alternating active/passive behavior + mechanical enhancement Unexpected passivation observed due to presence of H2O2 Passivation due to catalytic action of Cu(II)-glycine on H2O2 decomposition Mechanism still not fully understood Passive behavior Equivalent polarization curves for copper dissolution and polishing in aqueous 10-2M glycine solutions containing different amounts of H2O2 at pH 4 S. Aksu & F. M. Doyle, CMP V, Ed. S. Seal, The Electrochemical Society, PV-2002-1 pp. 79-90 (2002) L. Wang & F. M. Doyle, Mat. Res. Soc. Symp. Proc. Vol. 767, F6.5.1 FLCC - CMP

23. EQCM of Surface Film • Surface film thickness and composition can be estimated at a given chemistry from charge and mass gain measured with EQCM • Rate of formation of surface film can be determined • Etch rates, polishing rates, and repassivation kinetics. (W. Lu, J. Zhang, F. Kaufman, & A. C. Hilliera, J. Electrochem. Soc., 152, B17-B22, 2005) L. Wang & F. M. Doyle, unpublished FLCC - CMP

24. net charge transfer = q0 iactive ipassive Interval between two asperity-copper contacts (t0) Asperity-copper interaction time Removal Rate Model By Faraday’s Law: (Mass copper removed)/(Area) = MCuq0/(nF) • Can be simplified to a bimodal form Function of pad asperity distribution, velocity, down-force FLCC - CMP

25. Other Considerations Other considerations: • Role of Inhibitors (BTA) • Particle Agglomeration • Surfactant behavior • Abrasive “chemical tooth” • Fluid dynamics • Mass transport More fundamental studies needed for: • Asperity-copper interaction to investigate – threshold pressure required for surface film removal; importance of interval between contacts • Surface film characterization • Verification of mechanically enhanced chemical reaction rates FLCC - CMP

26. Future Goals • Continued development of integrated copper CMP model development, accounting for local variations due to wafer features. • Integrated model for ECMP • Experiments to investigate Asperity-wafer interaction. • Process design for abrasive-less slurries. FLCC - CMP

27. EPD + 30sec High Selectivity CMP Initial Oxide Topography Start Layout pattern density Layout line width HDPCVD model Initial topography Topography at time t Edge factor nitride exposure ? HDPCVD model Effective local contact pressure no yes Layout pattern density Effective real pattern density Real pattern density at time t nitride exposure ? slurry characteristics for nitride yes Local removal rate no slurry characteristics for oxide simulation over ? no Topography at time t+dt yes END Chip Scale Modeling of High Selectivity STI CMP, Linking HDP-CVD Oxide Topography Simulation procedure and results Future Goals • Model calibration with test CMP results • Model application for different pattern & verification • Linking with other process models • Stand-alone software FLCC - CMP

28. A B D C Open Cell Closed Cell CMP Pad Micro Feature Design with Sensor Integration Geometry Optimization • Open Cell: slurry overflow and slurry will not fill the interface at the contact level. • Closed cell: restrict slurry flow and pressure differentials will build, causing hydroplaning. • Type C has show the best performance Future Goals • Validate lower peak forces with micro scale sensors • Pad Sensor • GHz range output signals • Wireless realtime monitoring • Fracture Sensor Test Wafer 0.25µm(Cu)/0.25µm (Low-K) pattern Type C Conventional FLCC - CMP