GaP/Si: Studying Semiconductor Growth Characteristics with Realistic Quantum-Chemical Models

  • Andreas Stegmüller
  • Ralf TonnerEmail author
Conference paper


The understanding of microscopic processes and properties is crucial for the development and efficient production of inorganic III/V semiconductor materials. Those materials are grown in chemical vapour deposition procedures where elementary steps have not yet been thoroughly understood. Ab initio calculations are capable to investigate those atomic and electronic properties. Modern implementations of Density Functional Theory were applied to study layered bulk structures, periodic surface properties and adatom transport on Si(001) and GaP-Si(001) materials. By increasing cell sizes and number of atoms to scales that only supercomputing facilities can handle, a realistic chemical environment can be modeled with increased structural degrees of freedom. Bulk supercells were constructed in order to model realistic interfaces between two thin films in the nanometer scale. Supercell models in slab geometry were set up and converged with respect to the volume of vacuum and number of relaxed atoms for an accurate description of slab surfaces. These studies enable a direct comparison to experimental studies on these materials.


Density Functional Theory Surface Reconstruction Vacuum Region Hessian Matrice Kinetic Monte Carlo Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors acknowledge the collaborative research training group (Graduiertenkolleg, DFG) 1782 “Functionalization of Semiconductors” as well as the Beilstein Institut, Frankfurt am Main, for financial and further support.


  1. 1.
    Jandieri, K., Kunert, B., Liebich, S., Zimprich, M., Volz, K., Stolz, W., Gebhard, F., Baranovski, S.D., Koukourakis, N., Gerhardt, N.C., Hofmann, M.R.: Phys. Rev. B 87(3), 035303 (2013). doi:10.1103/PhysRevB.87.035303CrossRefGoogle Scholar
  2. 2.
    Lange, C., Chatterjee, S., Kunert, B., Volz, K., Stolz, W., Rühle, W.W., Gerhardt, N.C., Hofmann, M.R.: Gain characteristics and lasing of Ga(NAsP) multi-quantum well structures. Phys. Status Solidi (C) 6(2), 576 (2009). doi:10.1002/pssc.200880360Google Scholar
  3. 3.
    Kunert, B., Volz, K., Koch, J., Stolz, W.: Appl. Phys. Lett. 88(18), 182108 (2006). doi:10.1063/1.2200758CrossRefGoogle Scholar
  4. 4.
    Kunert, B., Volz, K., Stolz, W.: Phys. Status Solidi (B) 244(8), 2730 (2007). doi:10.1002/pssb.200675609Google Scholar
  5. 5.
    Liebich, S., Zimprich, M., Beyer, A., Lange, C., Franzbach, D.J., Chatterjee, S., Hossain, N., Sweeney, S.J., Volz, K., Kunert, B., Stolz, W.: Appl. Phys. Lett. 99(7), 071109 (2011). doi:10.1063/1.3624927CrossRefGoogle Scholar
  6. 6.
    Németh, I., Kunert, B., Stolz, W., Volz, K.: J. Cryst. Growth 310(7–9), 1595 (2008). doi:10.1016/j.jcrysgro.2007.11.127CrossRefGoogle Scholar
  7. 7.
    Volz, K., Beyer, A., Witte, W., Ohlmann, J., Németh, I., Kunert, B., Stolz, W.: J. Cryst. Growth 315(1), 37 (2011). doi:10.1016/j.jcrysgro.2010.10.036CrossRefGoogle Scholar
  8. 8.
    Liang, D., Bowers, J.E.: Nat. Photonics 4(8), 511 (2010). doi:10.1038/nphoton.2010.167CrossRefGoogle Scholar
  9. 9.
    Dürr, M., Biedermann, A., Hu, Z., Höfer, U., Heinz, T.F.: Science 296(5574), 1838 (2002). doi:10.1126/science.1070859CrossRefGoogle Scholar
  10. 10.
    Kunert, B., Zinnkann, S., Volz, K., Stolz, W.: J. Cryst. Growth 310(23), 4776 (2008). doi:10.1016/j.jcrysgro.2008.07.097CrossRefGoogle Scholar
  11. 11.
    Beyer, A., Ohlmann, J., Liebich, S., Heim, H., Witte, G., Stolz, W., Volz, K.: J. Appl. Phys. 111(8), 083534 (2012). doi:10.1063/1.4706573CrossRefGoogle Scholar
  12. 12.
    Saxler, A., Walker, D., Kung, P., Zhang, X., Razeghi, M., Solomon, J., Mitchel, W.C., Vydyanath, H.R.: Appl. Phys. Lett. 71(22), 3272 (1997). doi:10.1063/1.120310CrossRefGoogle Scholar
  13. 13.
    Fukuda, Y., Kobayashi, T., Mochizuki, S.: Appl. Surf. Sci. 176, 218 (2001)CrossRefGoogle Scholar
  14. 14.
    Hohenberg, P., Kohn, W.: Phys. Rev. 136(3B), B864 (1964)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Kohn, W., Sham, L.J., Others: Phys. Rev. 140(4A), A1133 (1965)MathSciNetGoogle Scholar
  16. 16.
    Kresse, G., Furthmüller, J.: Phys. Rev. B Condens. Matter 54(16), 11169 (1996)CrossRefGoogle Scholar
  17. 17.
    Kresse, G., Furthmüller, J.: Comput. Mater. Sci. 6(15) p. 15 (1996)Google Scholar
  18. 18.
    Schmidt, W.G., Bernholc, J., Bechstedt, F.: Appl. Surf. Sci. 166, 179 (2000)CrossRefGoogle Scholar
  19. 19.
    Hafner, J.: J. Comput. Chem. 29(13), 2044 (2008). doi:10.1002/jccCrossRefGoogle Scholar
  20. 20.
    Kempisty, P., Krukowski, S., Strak, P., Sakowski, K.: J. Appl. Phys. 106(5), 054901 (2009). doi:10.1063/1.3204965CrossRefGoogle Scholar
  21. 21.
    Perdew, J., Burke, K., Ernzerhof, M.: Phys. Rev. Lett. 77(18), 3865 (1996)CrossRefGoogle Scholar
  22. 22.
    Grimme, S.: Wiley Interdiscip. Rev.: Comput. Mol. Sci. 1(2), 211 (2011). doi:10.1002/wcms.30Google Scholar
  23. 23.
    Grimme, S., Ehrlich, S., Goerigk, L.: J. Comput. Chem. 32, 1456 (2011). doi:10.1002/jccCrossRefGoogle Scholar
  24. 24.
    Stegmüller, A., Rosenow, P., Tonner, R.: Phys. Chem. Chem. Phys. 16, 17018 (2014)CrossRefGoogle Scholar
  25. 25.
    Mattsson, A.E., Schultz, P.A., Desjarlais, M.P., Mattsson, T.R., Leung, K.: Model. Simul. Mater. Sci. Eng. 13(1), R1 (2005). doi:10.1088/0965-0393/13/1/R01CrossRefGoogle Scholar
  26. 26.
    Krukowski, S., Kempisty, P., Strak, P.: Cryst. Res. Technol. 44(10), 1038 (2009). doi:10.1002/crat.200900510CrossRefGoogle Scholar
  27. 27.
    Hashemifar, S., Kratzer, P., Scheffler, M.: Phys. Rev. B 82(21), 1 (2010). doi:10.1103/PhysRevB.82.214417CrossRefGoogle Scholar
  28. 28.
    Blöchl, P.: Phys. Rev. B 50(24), 17953 (1994)CrossRefGoogle Scholar
  29. 29.
    Kresse, G., Joubert, D.: Phys. Rev. B 59(3), 11 (1999)CrossRefGoogle Scholar
  30. 30.
    Kittel, C.: Introduction to Solid State Physics. Wiley, New York (1996)Google Scholar
  31. 31.
    Reuter, K.: First-Principles Kinetic Monte Carlo Simulations for Heterogeneous Catalysis: Concepts, Status, and Frontiers. In: Deutschmann, O. (ed.) Modeling Heterogeneous Catalytic Reactions: From the Molecular Process to the Technical System, Chap. 3, p. 71ff. Wiley-VCH, Weinberg (2009)Google Scholar
  32. 32.
    Stampfl, C.: Catal. Today 105(1), 17 (2005). doi:10.1016/j.cattod.2005.04.015CrossRefGoogle Scholar
  33. 33.
    Reuter, K., Frenkel, D., Scheffler, M.: Phys. Rev. Lett. 93(11), 1 (2004). doi:10.1103/PhysRevLett.93.116105CrossRefGoogle Scholar
  34. 34.
    Fritsch, J., Pavone, P.: Surf. Sci. 344(1–2), 159 (1995). doi:10.1016/0039-6028(95)00802-0CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Fachbereich ChemiePhilipps-Universität MarburgMarburgGermany

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