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Process control model for growth rate of molecular beam epitaxy of MgO (111) nanoscale thin films on 6H-SiC (0001) substrates

  • Ghulam Moeen Uddin
  • Katherine S. Ziemer
  • Abe Zeid
  • Yung-Tsun Tina Lee
  • Sagar KamarthiEmail author
ORIGINAL ARTICLE
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Abstract

Magnesium oxide (MgO) is a good candidate for an interface layer in multifunctional metal-oxide nanoscale thin-film heterostructures due to its high breakdown field and compatibility with complex oxides through O bonding. In this research, molecular beam epitaxy (MBE) is used to deposit 10 nm to 15 nm MgO single-crystal films on silicon carbide with hexagonal polytype 6H (6H-SiC) to serve as an interface layer for effective integration of functional oxides. In this work, the effect of MBE process control variables on the growth rate of the MgO film measured in nanometers per minute is investigated. Experiments are conducted at various process conditions and the resulting MgO film growth rate at each combination of process conditions is measured. The process control variables studied are the substrate temperature (100 °C – 300 °C), magnesium source temperature (328 °C – 350 °C), plasma intensity (0 mV – 550 mV), and percentage oxygen on the starting surface of 6H-SiC substrate (9 % – 13 %) after the substrate is prepared by high-temperature hydrogen etching. The film thickness is computed using the effective attenuation length (EAL) of silicon photoelectron peak intensity as measured by x-ray photoelectron spectroscopy (XPS). The film thickness is converted to growth rate by dividing it with the duration of film growth. Using the experimental data, a neural network model is developed to estimate growth rate for any given process variable combination. From this neural network model, multiple replications of data were generated to conduct a 3-level full factorial design of experiments and response surface-based analysis. The study reveals that the plasma intensity has the most significant influence on growth rate. The results indicate that growth rate is relatively low on high-quality substrates with √3 × √3 R30° reconstructed 6H-SiC (0001) surface with optimum oxygen content (approximately 10 %); in contrast, the growth rate is relatively high on substrates with high surface roughness and excessive oxygen on the starting substrate surface.

Keywords

Nanoscale manufacturing Manufacturing process modeling Molecular beam epitaxy Magnesium oxide nanoscale thin films Functional oxide heterostructures Neural networks Interface engineering Data analytic Smart manufacturing 

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Notes

Compliance with ethical standards

Disclaimer

Certain commercial software or equipment is identified in this article in order to aid understanding. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

References

  1. 1.
    Doolittle WA, Carver AG, Henderson W (2005) Molecular beam epitaxy of complex metal-oxides: where have we come, where are we going, and how are we going to get there? Journal of Vacuum Science & Technology, B: Microelectronics and Nanoscalemeter Structures-Processing, Measurement, and Phenomena 23(3):1272–1276CrossRefGoogle Scholar
  2. 2.
    Goodrich TL (2008) Atomistic investigation into the interface engineering and heteroepitaxy of functional oxides on hexagonal silicon carbide through the use of a magnesium oxide template layer for the development of a multifunctional heterostructure, Dissertation, Northeastern UniversityGoogle Scholar
  3. 3.
    Craft HS, Ihlefeld JF, Losego MD, Collazo R, Sitar Z, Maria J-P (2006) MgO epitaxy on GaN (0002) surfaces by molecular beam epitaxy. Appl Phys Lett 88(21):212906.1–212906.3CrossRefGoogle Scholar
  4. 4.
    Chen Z, Yang A, Geiler A, Harris VG, Vittoria C, Ohodnicki PR, Goh KY, McHenry ME, Cai Z, Goodrich TL, Ziemer KS (2007) Epitaxial growth of M-type Ba-hexaferrite films on MgO (111) SiC (0 0 01) with low ferromagnetic resonance linewidths. Appl Phys Lett 91(18):182505.1–182505.3Google Scholar
  5. 5.
    Cai Z (2010) Molecular beam epitaxy integration of magnetic ferrites with wide bandgap semiconductor 6H-SiC for next generation microwave and spintronic devices, Dissertation, Northeastern UniversityGoogle Scholar
  6. 6.
    Goodrich TL, Cai Z, Ziemer KS (2008) Stability of MgO(111) films grown on 6H-SiC(0001) by molecular beam epitaxy for two-step integration of functional oxides. Appl Surf Sci 254:3191–3199CrossRefGoogle Scholar
  7. 7.
    Goodrich TL, Cai Z, Losego MD, Maria J-P, Kourkoutis LF, Muller DA, Ziemer KS (2008) Improved epitaxy of barium titanate by molecular beam epitaxy. Journal of Vacuum Science Technology B 26:024803CrossRefGoogle Scholar
  8. 8.
    Cai Z, Chen Z, Goodrich TL, Harris VG, Ziemer KS (2007) Chemical and structural characterization of barium hexaferrite films deposited on 6H-SiC with and without MgO/BaM interwoven layers. J Cryst Growth 307:321CrossRefGoogle Scholar
  9. 9.
    Uddin GM, Ziemer KS, Zeid A, Kamarthi S (2012) “Study of lattice strain propagation in molecular beam epitaxy of nanoscale scale magnesium oxide thin film on 6H-SiC substrates using neural network computer models.” Proceedings of the International Mechanical Engineering Congress and Exposition Houston, Texas, USAGoogle Scholar
  10. 10.
    Bernhardt J, Schardt J, Starke U, Heinz K (1999) Epitaxially ideal oxide semiconductor interfaces: silicate adlayers on hexagonal (0001) and (000-1) SiC surfaces. Appl Phys Lett 74:1084–1086CrossRefGoogle Scholar
  11. 11.
    Ramachandran V, Brandy MF, Smith AR, Feenstra RM, Greve DW (1996) Preparation of atomically flat surfaces on silicon carbide using hydrogen etching. J Electron Mater 27:308–312CrossRefGoogle Scholar
  12. 12.
    Goodrich TL, Parisi J, Leong J, Ziemer KS (2005) "SiC surface preparation by hydrogen cleaning for high-temperature, high-power device integration," Proceedings of AICHE Annual Meeting, Cincinnati, OH, p. 135Google Scholar
  13. 13.
    Lazarov VK, Cai Z, Yoshida K, Zhang KH, Weinert M, Ziemer KS, Hasnip PJ (2011) Dynamically stabilized growth of polar oxides: the case of MgO(111). Phys Rev Lett 107(5):056101CrossRefGoogle Scholar
  14. 14.
    Nation Institute of Standards and Technology Standard’s Reference Database 82, https://www.nist.gov/srd/nist-standard-reference-database-82
  15. 15.
    Wagner CD, Riggs WM, Davis LE, Moulder JF, Muilenberg GE (1979) Handbook of X-ray photoelectron spectroscopy: Perkin-Elmer Corporation, NISTGoogle Scholar
  16. 16.
    Haykins SP (2009) Neural networks and learning machines. Prentice Hall/Pearson, New YorkGoogle Scholar
  17. 17.
    Uddin GM, Cai Z, Ziemer KS, Zeid A, Kamarthi S (2010) Analysis of molecular beam epitaxy process for growing nanoscalescale magnesium oxide films. ASME Journal of Manufacturing Science and Engineering 132(3):030913.1–030913.9CrossRefGoogle Scholar
  18. 18.
    Uddin GM, Ziemer KS, Zeid A, Kamarthi S (2015) Monte Carlo study of the molecular beam epitaxy process for manufacturing magnesium oxide nano-scale films. IIE Trans 47:125–140CrossRefGoogle Scholar
  19. 19.
    Uddin GM, Ziemer KS, Zeid A, Kamarthi S (2013) Monte Carlo study of the high temperature hydrogen cleaning process of 6H-silicon carbide for subsequent growth of nano scale metal oxide films. International Journal of Nanoscalemanufactruing 9(5/6):407–430CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Ghulam Moeen Uddin
    • 1
  • Katherine S. Ziemer
    • 2
  • Abe Zeid
    • 1
  • Yung-Tsun Tina Lee
    • 3
  • Sagar Kamarthi
    • 1
    Email author
  1. 1.Department of Mechanical and Industrial EngineeringNortheastern UniversityBostonUSA
  2. 2.Chemical Engineering DepartmentNortheastern UniversityBostonUSA
  3. 3.National Institute of Standards and TechnologyGaithersburgUSA

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