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Self-Organized Surface Nanopatterning by Ion Beam Sputtering

  • Javier Muñoz-García
  • Luis Vázquez
  • Rodolfo Cuerno
  • José A. Sánchez-García
  • Mario Castro
  • Raúl Gago
Chapter
Part of the Lecture Notes in Nanoscale Science and Technology book series (LNNST, volume 5)

Abstract

The production of self-organized surface nanopatterns by ion beam sputtering (IBS) at low (<10 keV) and intermediate (10–100 keV) energies has emerged in the last decade as a promising bottom-up nanostructuring tool. The technique is remarkably universal, being applicable to metals, semiconductors or insulators, and it enables large degree of control over the main pattern features with high throughput (it requires low process time and can be used over extended areas). However, there is a wide scatter in the experimental results obtained as a function of system type and process parameters. In parallel, diverse theoretical models have been developed that differ in their capabilities to reproduce such a wide range of experimental features. We provide an overview of the most recent studies on the production of nanoripple, nanohole and nanodot periodic nanostructures by IBS, with special attention to the comparison between experiments and (continuum) models, and with a focus on those issues that remain open or, at least, ambiguous. The pattern properties to be considered are those of potential increased technological importance, such as the variation of size, shape, distance and ordering of the nanostructures as a function of parameters such as ion energy, target temperature and sputtering time (i.e., fluence). Finally, reported and proposed applications of IBS nanopatterns are briefly presented showing, in this way, the high-potential functionality of IBS nanostructured surfaces.

Keywords

Monte Carlo Highly Orient Pyrolytic Graphite Highly Orient Pyrolytic Graphite Surface Ripple Pattern Grazing Incidence Diffraction 
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

Acknowledgments

We are pleased to acknowledge collaborations and exchange with a number of colleagues, in particular JM Albella, MC Ballesteros, A-L Barabási, M Camero, T Chini, M Feix, AK Hartmann, R Kree, M Makeev, TH Metzger, O Plantevin, M Varela and EO Yewande. We would like to thank specially F Alonso for his help in the ripple experiments on Si at 40 keV shown in Section 3.

Our work has been partially supported by Spanish grants Nos. FIS2006-12253-C06 (-01, -02, -03, -06) from Ministerio de Educación y Ciencia (MEC), CCG06-UAM/MAT-0040 from Comunidad Autónoma de Madrid (CAM) and Universidad Autónoma de Madrid, CCG08-CSIC/MAT-3457 from CAM and CSIC, UC3M-FI-05-007 and CCG06-UC3M/ESP-0668 from CAM and Universidad Carlos III de Madrid, and finally S-0505/ESP-0158 from CAM. RG also acknowledges financial support from the “Ramón y Cajal” program (MEC).

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Javier Muñoz-García
    • 1
  • Luis Vázquez
    • 2
  • Rodolfo Cuerno
    • 1
  • José A. Sánchez-García
    • 2
  • Mario Castro
    • 3
  • Raúl Gago
    • 2
  1. 1.Departamento de Matemáticas and Grupo Interdisciplinar de SistemasComplejos (GISC), Universidad Carlos III de MadridE-28911 LeganésSpain
  2. 2.Consejo Superior de Investigaciones CientíficasInstituto de Ciencia de Materiales de MadridE-28049 MadridSpain
  3. 3.Universidad Pontificia Comillas de MadridEscuela Técnica Superior de Ingeniería and GISCE-28015 MadridSpain

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