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Applied Physics A

, 122:1027 | Cite as

Structural, electrical, band alignment and charge trapping analysis of nitrogen-annealed Pt/HfO2/p-Si (100) MIS devices

  • Arvind Kumar
  • Sandip Mondal
  • K. S. R. Koteswara Rao
Article

Abstract

Low leakage current density and high relative permittivity (dielectric constant) are the key factor in order to replace the SiO2 from Si-based technology toward its further downscaling. HfO2 thin films received significant attention due to its excellent optoelectronic properties. In this work, ultra-thin (17 nm) HfO2 films on Si substrate are fabricated by RF sputtering. As deposited films are amorphous in nature and in order to get the reasonable high dielectric constant, the films are annealed (700 °C, 30 min) in nitrogen environment. A high refractive index (2.08) and small grain size (~10) nm were extracted from ellipsometry and XRD, respectively. The AFM study revealed a small RMS surface roughness 9 Å. For electrical characterization, films are integrated in metal–insulator–semiconductor capacitors structure. The oxide capacitance (C ox), flat band capacitance (C FB), flat band voltage (V FB), and oxide-trapped charges (Q ot) calculated from high-frequency (1 MHz) CV curve are 490, 241 pF, 1.21 V and 1.8 × 1012 cm−2, respectively. The dielectric constant calculated from accumulation capacitance is 17. The films show a low leakage current density 6.8 × 10−9 A/cm2 at +1 V, and this is due to the reduction in oxygen vacancies concentration as we performed annealing in N2 environment. The band gap of the films is estimated from O 1s loss spectra and found 5.7 eV. The electron affinity (χ) and HfO2/Si barrier height (conduction band offset) extracted from UPS spectra are 1.88 and 2.17 eV, respectively. A trap state with 0.99 eV activation energy below the conduction band edge is found and assigned to the fourfold coordinated oxygen vacancy in m-HfO2.

Keywords

HfO2 Pentacene Leakage Current Density Conduction Band Edge Oxygen Vacancy Concentration 
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.

Notes

Acknowledgements

AK would like to thank UGC, New Delhi, for the research fellowship.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Arvind Kumar
    • 1
  • Sandip Mondal
    • 1
  • K. S. R. Koteswara Rao
    • 1
  1. 1.Department of PhysicsIndian Institute of ScienceBangaloreIndia

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