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FLCC Seminar

FLCC Seminar. Title: Effects of CMP Slurry Chemistry on Agglomeration of Alumina Particles and Copper Surface Hardness Faculty: Jan B. Talbot Student: Robin Ihnfeldt Department: Chemical Engineering University: University of California, San Diego. Introduction.

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FLCC Seminar

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  1. FLCC Seminar Title: Effects of CMP Slurry Chemistry on Agglomeration of Alumina Particles and Copper Surface Hardness Faculty: Jan B. Talbot Student: Robin Ihnfeldt Department: Chemical Engineering University: University of California, San Diego CMP

  2. Introduction Integrated Circuit manufacturing requires material removal and global planarity of wafer surface – Chemical Mechanical Planarization (CMP) • CMP slurries provide material removal by: • Mechanical abrasion • Nanometer sized abrasive particles (alumina) • Chemical reaction • Chemical additives (glycine, H2O2, etc.) • Material Removal Rate (MRR) is affected by: • Abrasive size and size distribution • Wafer surface hardness • Cu is the interconnect of choice- our research focus CMP

  3. CMP Schematic P = 1.5-13 psi V= 20-90 rpm slurry (100-300 ml/min) wafer carrier polishing pad wafer (polyurethane) platen Cu MRR= 50 - 600 nm/min Planarization time = 1- 3 min RMS roughness = < 1 nm wafer Particle concentration = 1 - 30 wt% Particle size = 50 - 1000 nm dia slurry polishing pad CMP

  4. Motivation • Better process control • Understand role of slurry chemistry (additives, pH, etc.) • Develop slurries to provide adequate removal rates and global planarity • Prediction of material removal rates (MRR) • Predictive CMP models - optimize process consumables • Improve understanding of effects of CMP variables • Reduce cost of CMP • Reduce defects • Control of abrasive particle size • Control of interactions between the wafer surface and the slurry CMP

  5. Research Approach • Experimental study of colloidal behavior of CMP slurries • Zeta potential and particle size distribution measurements • Function of pH, ionic strength, additives • Alumina particles in presence of common Cu CMP additives • Alumina particles in presence of copper nanoparticles • Measurement of surface hardness as function of slurry chemistry • Develop comprehensive model (Lou & Dornfeld, IEEE, 2003) • Mechanical effects (Dornfeld et al., UCB) • Electrochemical effects (Doyle et al., UCB) • Colloidal effects (Talbot et al., UCSD) CMP

  6. Common Cu Slurry Additives Robin Ihnfeldt and J.B. Talbot. J. Electrochem. Soc., 153, G948 (2006). Tanuja Gopal and J.B. Talbot. J. Electrochem. Soc., 153, G622 (2006). CMP

  7. CuL+, Cu2+,Cu+ CuO, Cu2O, CuL2 Cu Cu CMP Chemical Reactions Dissolution: Cu(s) + HL  CuL+(aq) + H+ + e Oxidation: 2Cu + H2O  Cu2O + 2H+ + 2e Oxide dissolution: Cu2O + 3H2O  2CuO22- + 6H+ +2e Complexation (to enhance solubility) Cu2+ + HL  CuL+ + H+ CMP

  8. Chemical PhenomenaChemistry of Glycine-Water System copper-water system [CuT]=10-5M copper-water-glycine system [LT]=10-1M, [CuT]=10-5M Ref.: Pourbaix (1957); (Aksu and Doyle (2002) CMP

  9. Abrasive particle Dissolution product Surface Colloidal Aspects of CMP • Particle – particle • Particle – surface • Particle – dissolution product • Surface – dissolution product CMP

  10. Experimental Procedure Slurry Abrasives • 40 wt% a-alumina slurry (from Cabot Corp.) • 150nm average aggregate diameter – 20nm primary particle diameter Common Copper CMP Slurry Additives • Glycine, EDTA, H2O2, BTA, SDS Copper nano-particles • Added 0.12 mM to simulate removal of copper surface during CMP • <100 nm in diameter (from Aldrich) Zeta Potential and Agglomerate Size Distribution • Brookhaven ZetaPlus • Zeta Potential – Electrophoretic light scattering technique (±2%) • Agglomerate Size – Quasi-elastic light scattering (QELS) technique (±1%) • All samples diluted to 0.05 wt% in a 1 mM KNO3 solution • Solution pH adjusted using KOH and HNO3 and ultrasonicated for 5 min prior to measuring CMP

  11. + Diffuse Layer + + + Shear Plane + + + + + Particle Surface + + a + + + + + + + + + + + + + Potential  Distance 1/ Electrical Double Layer Potential at surface usually stems from adsorption of lattice ions, H+ or OH- Potential is highly sensitive to chemistry of slurry Slurries are stable when all particles carry same charge; electrical repulsion overcomes van de Waals attractive forces If potentials are near zero, abrasive particles may agglomerate = ionic strength Zeta Potential CMP

  12. Zeta Potential Zeta Potential - Potential at the Stern Layer Electrophoresis – Zeta potential estimated by applying electric field and measuring particle velocity Surface charge on metal oxides is pH dependant: M-OH + OH- → M-O- + H2O M-OH + H+ → M-OH2+ • IEP at z = 0 • Slurries are stable when |z | > 25 mV Cabot alumina without additives in 10-3M KNO3 solution (bars indicate standard deviation of agglomerate size distribution) CMP

  13. Zeta Potential Cabot alumina in 10-3M KNO3 solution with and without 0.12mM copper • IEP ~6.5 with and without copper • IEP~9.2 for a-alumina from literature* • Impurities (NO3-, SO42-, etc.) may lower IEP** • At high pH values magnitude of zeta potential lower with copper than without *M.R. Oliver, Chemical-Mechanical Planarization of Semiconductor Material, Springer-Verlag, Berlin (2004). **G.A. Parks, Chem. Tevs., 65, 177 (1965). CMP

  14. Agglomerate Size Distribution Cabot alumina dispersion in 1mM KNO3 solution with (red) and without (blue) 0.12 mM copper and without chemical additives • pH 2 – presence of copper causes decrease in agglomeration • pH 7 – presence of copper causes increase in agglomeration CMP

  15. Copper-Alumina-Water System Potential-pH for Copper-water System [Cu]=10-4M at 250C and 1atm (M. Pourbaix 1957) IEP of CuO ~ 9.5* ■ Agglomeration behavior is consistent with the Pourbaix diagram Average agglomerate size of bimodal distributions in a 1 mM KNO3 solution *G.A. Parks, Chem. Tevs., 65, 177 (1965). Robin Ihnfeldt and J.B. Talbot. J. Electrochem. Soc., 153, G948 (2006). CMP

  16. Zeta Potential Cabot alumina in 0.1M glycine and 10-3M KNO3 solution with and without 0.12mM copper • IEP ~6.5 without copper • IEP~9.2 increased with copper *M.R. Oliver, Chemical-Mechanical Planarization of Semiconductor Material, Springer-Verlag, Berlin (2004). **G.A. Parks, Chem. Tevs., 65, 177 (1965). CMP

  17. Copper-Glycine-Water System Potential-pH for Copper-Glycine-Water System* [Cu]=10-4M, [Glycine]=10-1M at 250C and 1atm • Agglomeration behavior is consistent with Pourbaix diagram Average agglomerate size of bimodal distributions in a 1 mM KNO3 solution with various additives *S. Aksu and F. M. Doyle, J. Electrochemical Soc., 148, 1, B51 (2006). CMP

  18. Measuring Wafer Hardness TriboScope Nanomechanical Testing system from Hysitron Inc. • 1 cm2 silicon wafer pieces sputter deposited with 30 nm Ta + 1000 nm Cu • 10 min exposure in 100 ml of slurry solution (without abrasives), then removed and dried with air and measured ■Considerations • Large applied load will increase indentation depth – • more likely for underlying layer to affect nanohardness measurements • Slurry solutions with high etch rates will decrease copper thickness – • thinner copper layer more likely for underlying layer to affect measurements Robin Ihnfeldt and J.B. Talbot. 210th Meeting Electrochem. Soc., Cancun, Mexico, Oct. 29-Nov. 3, 602, 1147 (2006). CMP

  19. Copper Surface in Solution Bulk metallic Cu H~ 2.3 GPa* Ta2O5 H~9 GPa Surface nanohardness of Cu on Ta/Si (100uN applied load) after exposure to 1mM KNO3 solution ■pH 2 – appears that state of surface is Cu metal with increase in nanohardness from underlying layer ■pH 7 and 12 – hardness less than that of bulk metallic Cu • Cupric hydroxide, Cu(OH)2, is most likely forming *S. Chang, T. Chang, and Y. Lee, J. Electrochemical Soc., 152, (10), C657 (2005). CMP

  20. Copper Surface in Solution Surface nanohardness of Cu on Ta/Si (100uN applied load) after exposure to 1mM KNO3 solution and other additives Film Growth Increased Hardness Glycine • Surface hardness is less than that of bulk Cu at pH 2 and 12 – • Glycine may interact with surface layer to decrease compactness • pH 7 appears to be Cu metal with increase due to underlying layer Glycine + H2O2 • H2O2 increases solubility of Cu-glycinate complex or increases Cu oxidation • Surface is less than bulk Cu at pH 2 and 7 – decrease in compactness due to glycine • pH 12 appears to be cuprous oxide, Cu2O CMP

  21. CMP Experiments Toyoda Polishing apparatus (UC Berkeley) • IC1000 polishing pad pre-conditioned for 20 minutes with diamond conditioner • Polished 2 min with Cabot alumina Silicon wafers (100 mm dia.) with 1 mm copper on 30 nm tantalum • Total of 18 wafers polished with various slurry chemistries and at various pH values CMP

  22. Experimental Copper CMP MRR MRR is <20 nm/min for all pH values without additives, with 0.1M glycine MRR is >100 nm/min for several pH values where both glycine and H202 are present CMP

  23. Vol N Force F & Velocity Slurry Concentration C Active Abrasive Size Xact Average Abrasive Size Xavg Wafer hardness Hw/ Slurry Chemicals & Wafer Materials Proportion of Active Abrasives Lou and Dornfeld CMP Model Basic Eqn. of Material Removal: MRR = N x Vol CMP

  24. Conclusions Colloidal Behavior • pH has greatest effect on colloidal behavior • Glycine acts as a stabilizing agent for alumina • Presence of Cu nanoparticles can increase or decrease agglomeration depending on the state of copper in solution • Agglomeration behavior with copper is consistent with potential-pH diagrams Nanohardness of Copper Surface • pH of the slurry affects copper surface hardness • Addition of chemical additives has large effect on the surface hardness • State of copper on surface is consistent with potential-pH diagrams • Under certain conditions glycine may cause decrease in copper surface hardness CMP

  25. Future Work • Continue to investigate effect of copper on zeta potential and particle size • Determine state of Cu in solution • Study agglomeration as a function of time • Initial hardness measurements show large differences in copper surface with pH and chemical addition • Determine reproducibility of hardness measurements • Determine state of Cu on surface • Modeling – Luo and Dornfeld Model* • Incorporate experimental measurements (hardness and agglomerate size distribution) into model and compare with experimental CMP data *J. Luo and D. Dornfeld, IEEE Trans. Semi. Manuf., 14, 112 (2001). CMP

  26. Acknowledgments • Funded by FLCC Consortium through a UC Discovery grant. We gratefully acknowledge the companies involved in the UC Discovery grant: Advanced Micro Devices, Applied Materials, Atmel, Cadence, Canon, Cymer, DuPont, Ebara, Intel, KLA-Tencor, Mentor Graphics, Nikon Research, Novellus Systems, Panoramic Technologies, Photronics, Synopsis, Tokyo Electron • Prof. Dornfeld and his research group at UC Berkeley for use of the CMP apparatus and model program • Prof. Talke and his research group at UCSD for the use of the Hysitron Instrument. CMP

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