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Nanopackaging of Si(100)H Wafer for Atomic-Scale Investigations

  • Delphine SordesEmail author
  • Aurélie Thuaire
  • Patrick Reynaud
  • Caroline Rauer
  • Jean-Michel Hartmann
  • Hubert Moriceau
  • Emmanuel Rolland
  • Marek Kolmer
  • Marek Szymonski
  • Corentin Durand
  • Christian Joachim
  • Séverine Chéramy
  • Xavier Baillin
Conference paper
Part of the Advances in Atom and Single Molecule Machines book series (AASMM)

Abstract

Ultra-high vacuum (UHV) investigations have demonstrated a successful development of atomic nanostructures. The scanning tunneling microscope (STM) provides surface study at the atomic scale. However, the surface preparation is a crucial experimental step and requires a complex protocol conducted in situ in a UHV chamber. Surface contamination, atomic roughness, and defect density must be controlled in order to ensure the reliability of advanced UHV experiments. Consequently, a packaging for nanoscale devices has been developed in a microelectronic clean room environment enabling the particle density and contaminant concentration control. This nanopackaging solution is proposed in order to obtain a Si(001)-(2×1):H reconstructed surface. This surface is protected by a temporary silicon cap. The nanopackaging process consists in a direct bonding of two passivated silicon surfaces and is followed by a wafer dicing step into 1-cm2 dies. Samples can be stored, shipped, and in situ opened without any additional treatment. A specific procedure has been developed in order to open the nanopackaged samples in a UHV debonder, mounted in the load-lock chamber of a low-temperature STM system (LT-STM). Statistical large scan LT-UHV-SEM images and LT-UHV-STM images have been obtained enabling the surface study at the atomic resolution.

Keywords

Scanning Tunneling Microscopy Scanning Tunneling Microscopy Image Reduce Pressure Chemical Vapor Deposition Scanning Tunneling Microscopy System Scanning Tunneling Microscopy Head 
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

This research has been supported by the 7th Framework Program of the European Union Collaborative Project ICT (Information and Communication Technologies) “Atomic Scale and Single Molecule Logic Gate Technologies” (ATMOL), contract number: FP7-270028. The CEA Institute, the CEMES, and the Polish National Science Center are acknowledged as well for financial support.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Delphine Sordes
    • 1
    Email author
  • Aurélie Thuaire
    • 2
    • 3
  • Patrick Reynaud
    • 2
    • 3
  • Caroline Rauer
    • 2
    • 3
  • Jean-Michel Hartmann
    • 2
    • 3
  • Hubert Moriceau
    • 2
    • 3
  • Emmanuel Rolland
    • 2
    • 3
  • Marek Kolmer
    • 4
  • Marek Szymonski
    • 4
  • Corentin Durand
    • 5
  • Christian Joachim
    • 5
  • Séverine Chéramy
    • 2
    • 3
  • Xavier Baillin
    • 2
    • 3
  1. 1.CEATech Midi-PyrénéesToulouseFrance
  2. 2.University Grenoble AlpesGrenobleFrance
  3. 3.CEA-LETIGrenobleFrance
  4. 4.Faculty of Physics, Astronomy and Applied Computer Science, Centre for Nanometer-Scale Science and Advanced Materials, NANOSAMJagiellonian UniversityKrakowPoland
  5. 5.PicoLabCEMES/CNRSToulouse CedexFrance

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