Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide


Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from about 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl2 and BCl3 produce the most boron atoms, while C2H4 and CH4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron–carbon ratio and provide a new design solution for other multi-element systems.


  1. [1]

    Ohnabe H, Masaki S, Onozuka M, et al. Potential application of ceramic matrix composites to aero-engine components. Compos Part A-Appl S 1999, 30: 489–496.

    Article  Google Scholar 

  2. [2]

    Inghels E, Lamon J. An approach to the mechanical behaviour of SiC/SiC and C/SiC ceramic matrix composites. J Mater Sci 1991, 26: 5411–5419.

    CAS  Article  Google Scholar 

  3. [3]

    Christin F. Design, fabrication, and application of thermostructural composites (TSC) like C/C, C/SiC, and SiC/SiC composites. Adv Eng Mater 2002, 4: 903–912.

    CAS  Article  Google Scholar 

  4. [4]

    Katoh Y, Snead LL, Henager CH, et al. Current status and recent research achievements in SiC/SiC composites. J Nucl Mater 2014, 455: 387–397.

    CAS  Article  Google Scholar 

  5. [5]

    Naslain R, Guette A, Rebillat F, et al. Boron-bearing species in ceramic matrix composites for long-term aerospace applications. J Solid State Chem 2004, 30: 489–496.

    Google Scholar 

  6. [6]

    Liu YS, Cheng LF, Zhang LT, et al. Oxidation protection of multilayer CVD SiC/B/SiC coatings for 3D C/SiC composite. Mat Sci Eng A-Struct 2007, 466: 172–177.

    Article  Google Scholar 

  7. [7]

    Sezer AO, Brand JI. Chemical vapor deposition of boron carbide. Mater Sci Eng B-Adv 2001, 79: 191–202.

    Article  Google Scholar 

  8. [8]

    Jacques S, Guette A, Langlais F, et al. C(B) materials as interphases in SiC/SiC model microcomposites. J Mater Sci 1997, 32: 983–988.

    CAS  Article  Google Scholar 

  9. [9]

    Ruggles-Wrenn MB, Wallis TA. Creep in interlaminar shear of an Hi-Nicalon™/SiC-B4C composite at 1300 °C in air and in steam. J Compos Mater 2019, 54: 1819–1829.

    Article  Google Scholar 

  10. [10]

    Deshpande SV, Gulari E, Harris SJ, et al. Filament activated chemical vapor deposition of boron carbide coatings. Appl Phys Lett 1994, 65: 1757–1759.

    CAS  Article  Google Scholar 

  11. [11]

    Karaman M, Sezgi NA, Dogu T, et al. Kinetic investigation of chemical vapor deposition of B4C on tungsten substrate. AICHE J 2006, 52: 4161–4166.

    CAS  Article  Google Scholar 

  12. [12]

    Karaman M, Sezgi NA, Dogu T, et al. Mechanism studies on CVD of boron carbide from a gas mixture of BCl3, CH4, and H2 in a dual impinging-jet reactor. AICHE J 2009, 55: 701–709.

    CAS  Article  Google Scholar 

  13. [13]

    Berjonneau J, Langlais F, Chollon G, et al. Understanding the CVD process of (Si)-B-C ceramics through FTIR spectroscopy gas phase analysis. Surf Coat Tech 2017, 201: 7273–7285.

    Article  Google Scholar 

  14. [14]

    Berjonneau J, Chollon G, Langlais F, et al. Deposition process of Si-B-C ceramics from CH3SiCl3/BCl3/H2 precursor. Thin Solid Films 2008, 516: 2848–2857.

    CAS  Article  Google Scholar 

  15. [15]

    Liu YS, Zhang LT, Cheng LF, et al. Uniform design and regression analysis of LPCVD boron carbide from BCl3-CH4-H2 system. Appl Surf Sci 2009, 255: 5729–5735.

    CAS  Article  Google Scholar 

  16. [16]

    Zeng B, Feng ZD, Li SW, et al. Microstructural study of oxidation of carbon-rich amorphous boron carbide coating. Front Mater Sci 2008, 2: 375–380.

    Article  Google Scholar 

  17. [17]

    Mollick PK, Venugopalan R, Srivastava D. CFD coupled kinetic modeling and simulation of hot wall vertical tubular reactor for deposition of SiC crystal from MTS. J Cryst Growth 2017, 475: 97–109.

    CAS  Article  Google Scholar 

  18. [18]

    Ni H, Lu S, Chen C. Modeling and simulation of silicon epitaxial growth in siemens CVD reactor. J Cryst Growth 2014, 404: 89–99.

    CAS  Article  Google Scholar 

  19. [19]

    Deck CP, Khalifa HE, Sammuli B, et al. Fabrication of SiC-SiC composites for fuel cladding in advanced reactor designs. Prog Nucl Energy 2012, 57: 38–45.

    CAS  Article  Google Scholar 

  20. [20]

    Reinisch G, Patel S, Chollon G, et al. Methyldichloroborane evidenced as an intermediate in the chemical vapour deposition synthesis of boron carbide. J Nanosci Nanotechnol 2011, 11: 8323–8327.

    CAS  Article  Google Scholar 

  21. [21]

    Li J, Qin H, Liu Y, et al. Effect of the SiCl4 flow rate on SiBN deposition kinetics in SiCl4-BCl3-NH3-H2-Ar environment. Materials 2017, 10: 627–637.

    Article  Google Scholar 

  22. [22]

    Kleijn CR. Modeling of Chemical Vapor Deposition of Tungsten Films. Boston, USA: Birkhäuser Basel, 1993.

    Book  Google Scholar 

  23. [23]

    Beek WJ, Muttzall KMK, van Heuven JW. Transport Phenomena, 2nd edn. New York, USA: John Wiley & Sons, 1999.

    Google Scholar 

  24. [24]

    Cuadros F, Cachadiña I, Ahumada W. Determination of Lennard-Jones interaction parameters using a new procedure. Mol Eng 1996, 6: 319–325.

    CAS  Article  Google Scholar 

  25. [25]

    Ge Y, Gordon M S, Battaglia F, et al. Theoretical study of the pyrolysis of methyltrichlorosilane in the gas phase. 1. Thermodynamics. J Phys Chem A 2007, 111: 1462–1474.

    CAS  Article  Google Scholar 

  26. [26]

    Ge Y, Gordon M S, Battaglia F, et al. Theoretical study of the pyrolysis of methyltrichlorosilane in the gas phase. 2. Reaction paths and transition states. J Phys Chem A 2007, 111: 1475–1486.

    CAS  Google Scholar 

  27. [27]

    Ge Y, Gordon M S, Battaglia F, et al. Theoretical study of the pyrolysis of methyltrichlorosilane in the gas phase. 3. Reaction Rate Constant Calculations. J Phys Chem A 2010, 114: 2384–2392.

    CAS  Google Scholar 

  28. [28]

    Liu Y, Su KH, Zeng QF, et al. Reaction paths of BCl3 + CH4 + H2 in the chemical vapor deposition process. Struct Chem 2012, 23: 1677–1692.

    CAS  Article  Google Scholar 

  29. [29]

    Liu Y, Su KH, Zeng QF, et al. Decomposition reaction rate of BCl3-CH4-H2 in the gas phase. Theor Chem Acc 2015, 134: 1–9.

    Article  Google Scholar 

  30. [30]

    Reinisch G, Leyssale JM, Vignoles GL. Theoretical study of the decomposition of BCl3 induced by a H radical. J Phys Chem A 2011, 115: 4786–4797.

    CAS  Article  Google Scholar 

  31. [31]

    Lee J H, Shin J, Realff M J. Machine learning: Overview of the recent progresses and implications for the process systems engineering field. Comput Chem Eng 2018, 114: 111–121.

    CAS  Article  Google Scholar 

  32. [32]

    Moreno R, Corona F, Lendasse A, et al. Extreme learning machines for soybean classification in remote sensing hyperspectral images. Neurocomputing 2014, 128: 207–216.

    Article  Google Scholar 

  33. [33]

    Basu A, Shuo S, Zhou H, et al. Silicon spiking neurons for hardware implementation of extreme learning machines. Neurocomputing 2013, 102: 125–134.

    Article  Google Scholar 

  34. [34]

    Benoît F, Heeswijk M, Miche Y, et al. Feature selection for nonlinear models with extreme learning machines. Neurocomputing 2013, 102: 111–124.

    Article  Google Scholar 

  35. [35]

    Feng S, Zhou H, Dong H, et al. Using deep neural network with small dataset to predict material defects. Mater Des 2019, 162: 300–310.

    Article  Google Scholar 

  36. [36]

    Kushvaha V, Kumar S A, Madhushri P, et al. Artificial neural network technique to predict dynamic fracture of particulate composite. J Compos Mater 2021, 54: 3099–3108.

    Article  Google Scholar 

  37. [37]

    Liu X, Gao C, Li P, et al. A comparative analysis of support vector machines and extreme learning machines. Neural Netw 2012, 33: 58–66.

    Article  Google Scholar 

  38. [38]

    Berjonneau J, Chollon G, Langlais F, et al. Deposition process of amorphous boron carbide from CH4/BCl3/H2 precursor. Proc Electrochem Soc 2006, 153: C795–C800.

    CAS  Article  Google Scholar 

  39. [39]

    Vandenbulcke L G. Theoretical and experimental studies on the chemical vapor deposition of boron carbide. Ind Eng Chem Res 2002, 24: 568–575.

    Article  Google Scholar 

  40. [40]

    Lartigue S, Cazajous D, Nadal M, et al. Study of boron carbides vapor-deposited under low pressure. In Proceedings of the Fifth European Conference on Chemical Vapor Deposition, 1985: 413–419.

    Google Scholar 

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We thank the National Key R&D Program of China (Grant No. 2017YFB0703200), National Natural Science Foundation of China (Grant Nos. 51702100 and 51972268), and China Postdoctoral Science Foundation (Grant No. 2018M643075) for the financial support.

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Correspondence to Qingfeng Zeng or Kang Guan.

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Zeng, Q., Gao, Y., Guan, K. et al. Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide. J Adv Ceram 10, 537–550 (2021).

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  • machine learning (ML)
  • computational fluid dynamic (CFD)
  • chemical vapor deposition
  • boron carbide
  • B/C ratio
  • kinetic mechanisms