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polysilicon deposition by Siemens process

http://www.str-soft.com/products/PolySim/

- 1. ISTC/CSTIC, Shanghai, Roman Talalaev 20.03.09 STR Group www.str-soft.com Detailed modeling of polysilicon deposition by Siemens process
- 2. Siemens process: established technology for polysilicon production Polysilicon deposition reactor (www.centrotherm-pv.de)
- 3. Siemens process: some figures • Cost of one run in a modern production-scale reactor (150 tons/year) is USD 200-400K. • Typical process time is from 7 to 10 days. • Increase of the reactor productivity by 5% at mid-size production plant (15-18 reactors) enables to get additional revenue of USD 300K/week (USD 15 Mio/year at current silicon prices). Polysilicon deposition reactor (www.centrotherm-pv.de)
- 4. Siemens process: established technology for polysilicon production • Experimental research is very expensive and time-consuming • Very limited capabilities for measurements Computational modeling can help: • increase the reactor productivity • reduce energy consumption • increase trichlorosilane-to-silicon conversion Polysilicon deposition reactor efficiency • optimize the material quality • reduce the cost and develop new advanced technologies
- 5. Siemens process modeling Detailed model of Siemens process: - turbulent gas flow, - resistive rod heating - current stabilization effect - thermal radiation - gas-phase and surface chemistry - a special criterion for prediction of porous structure formation: “popcorn” - gas-phase particle formation (in work) 3D computational model of a polysilicon deposition reactor
- 6. Chemical balance of Siemens process SiHCl3 (TCS), SiH2Cl2 (DCS), H2 SiHCl3, SiCl4, SiH2Cl2, H2, HCl, SiCl2, SiCl3, SiHCl + SiCl4, SiHCl3, SiH2Cl2, H2, HCl Silicon Process model includes most important gas-phase species
- 7. Kinetics of the gas-phase reactions 1 2 3 4 SiCl2 SiH2Cl2 21 SiHCl3 11 SiHCl 31 HCl 41 SiCl3 32 SiCl4 Cl 42 H2 22 12 H 11: SiHCl3 = SiCl2 + HCl 12: H2 + M = 2H + M 21: SiCl2 + H2 = SiH2Cl2 22: H + HCl = H2 + Cl Gas-phase chemistry mechanism 31: SiH2Cl2 = SiHCl + HCl includes various species and 32: SiCl2 + Cl = SiCl3 pathways 41: 2SiCl3 = SiCl2 + SiCl4 42: SiCl3 + HCl = SiCl4 + H
- 8. Kinetics of the surface reactions Gas-phase species SiH2Cl2, SiHCl3 SiCl4 H,Cl H2, HCl SiCl2 SiCl3 Adsorbed Cl, H, Si Precursors Kinetics is important in silicon deposition modeling
- 9. How to increase reactor productivity ? Ugr, 12 µm/min 11 Cl/Si=3 Т = 1400К 10 Silicon growth rate 9 dependence on Cl/H and 8 Cl/Si=3.3 7 Cl/Si ratios in the gas-phase 6 5 4 3 Cl/Si=3.5 2 1 Cl/Si=4 0 Cl/Si=3.7 -1 -2 0 0.25 0.5 0.75 1 Cl/H Productivity depends on gas mixture composition and temperature
- 10. How to minimize energy consumption ? Energy 200 consumption, kwh/kg 150 100 50 Rod diameter, mm 0 0 50 100 150 Energy consumption depends on rod size, optical properties of reactor walls, gas- phase mixture composition, temperature, flow velocity, number of the rods.
- 11. High purity silicon production without popcorn-like structure Scale of surface roughness smooth growth popcorn A special criterion analyzes the possibility of cavity formation between neighboring grains in dependence on local heat- and mass–transport conditions
- 12. Flow dynamics at different growth stages Flow dynamics significantly affects the deposition rate Flow velocity should be optimized for different rod diameters
- 13. Modeling results: 18-rod reactor Diameter variation Prediction of the local diameter variation for two rings of rods
- 14. Modeling results: 24-rod reactor Local temperature distribution for different gas injection schemes
- 15. Modeling results: temperature and growth rate distributions Reactors with 36-48 rods have been computed.
- 16. Cl/Si ratio and temperature distribution in a horizontal cross-section of a 36-rod reactor Cl/Si T, K Gas temperature in the reactor volume is much lower than at the rods and it is nearly uniform
- 17. Way to parametric optimization: 1. Model of Siemens process was developed based on experience of Si epitaxy and polycrystalline growth 2. 3D computations determine all the reactor characteristics. 3. But 3D computations are very long and and require a lot of simulation expertise, this makes parametric optimization not effective. 4. Engineering tool “PolySim” has been developed, it utilizes the knowledge gained from 3D computations, but makes computations much faster and efficient keeping the good accuracy.
- 18. Simulator of Siemens process Growth rate Instantaneous productivity Energy consumption Silicon conversion Number of the rods Popcorn appearance Rod diameter Scale of surface non-uniformity Rod height Electrical current Diameter and height of the chamber Total voltage Gas flow rates Total power POLYSIM Radiation losses Average rod surface temperature Convection losses Average gas velocity Heat losses with output gases Pressure Output gas flow rates Side wall emissivity Volume gas temperature The mean cooling liquid temperature Re, Gr·Pr Thickness of viscous sublayer Thickness of stainless steel walls Inner wall temperature Cl/Si in output gas mixture Ror center temperature Characteristics of the whole process Released November 2007, now used by the most of large-scale Characteristics in a local point polysilicon producers (China, Europe, Japan, South Korea, USA)
- 19. PolySim results : reactor characteristics in dependence on the TCS flow rate and pressure 36 rods, 70 mm, T = 1350 K, εwall = 0.7 40 200 1 atm 35 3 atm 6 atm 30 150 10 atm Energy cost, kWt*h/kg Silicon output, kg/h 25 20 100 15 1 atm 3 atm 10 50 6 atm 10 atm 5 0 0 0 5 10 15 20 25 0 5 10 15 20 25 TCS flow rate, kmol/h TCS flow rate, kmol/h Energy consumptions demonstrate complicated dependence on pressure
- 20. PolySim results : reactor characteristics in dependence on temperature and pressure 36 rods, 70 mm, TCS = 10 kmol/h , εwall = 0.7, V = 1.5 m/sec 200 70 180 60 1 atm 3 atm 6 atm 160 Energy cost, kWt*h/kg 50 10 atm Silicon output, kg/h 140 40 120 30 100 20 1 atm 80 3 atm 10 6 atm 10 atm 60 0 1300 1400 1500 1600 1200 1300 1400 1500 1600 Temperature, K Temperature, K • Energy consumptions demonstrate complicated dependence on pressure and temperature • Optimal pressure depends on temperature
- 21. Modeling Optimization Capabilities (based on existing experience): 1. Reactor productivity increase by 30% by improvement of the nozzle design and their arrangement, gas injection schedule, proper gas mixture composition choice. 2. Transfer of a recipe to popcorn-free deposition conditions. 3. New process concept of “reactor cluster” (patent pending) promises further 40% productivity increase .

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http://www.str-soft.com/products/PolySim/