Deposition of SnO2-Based Thin Films By Reactive DC Magnetron Sputtering for Gas Sensing Applications

  • J. Miguel Correia-Pires
  • Vasco Teixeira
  • J. B. Almeida
Part of the NATO Science Series book series (NAII, volume 102)


Tin dioxide is a semiconductor with good sensitivity to a wide variety of gases. It has been produced by several thin and thick film techniques, and successully used in gas sensor devices. Some of the parameters affecting gas sensing response are already known and may be used to guide the layer production process to improve gas sensing performance of the fabricated devices.

Magnetron sputtering is a reliable and environmentally clean technique, suitable to produce thin film oxide materials, like SnO2. Layers with widely different characteristics may be obtained by varying the deposition parameters. Namely, is has been verified that composition, grain size, porosity and surface roughness may be varied within the ranges reported to correspond to best gas sensitivity.

The influence of the deposition temperature, total pressure and magnetic flux density on the thin film characteristics have been studied and it was verified that it is possible to get layers with characteristics similar to the ones produced using other techiques and showing high sensitivity. Deposition rates and film density have been determined using data obtained from mass difference measurements, optical transmission spectra and scanning electron microscopy (SEM). Structural properties — crystal structure and orientation, surface morphology and grain size — have been studied using data from atomic force microscopy (AFM), SEM and X-ray diffraction (XRD). The coatings bulk and surface composition was investigated using energy dispersive X-ray analysis (EDX) and X-ray photoelectron spectroscopy (XPS).


Substrate Temperature Oxygen Partial Pressure Deposition Pressure Sn02 Nanoparticles Mass Difference Measurement 
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.


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Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • J. Miguel Correia-Pires
    • 1
  • Vasco Teixeira
    • 1
  • J. B. Almeida
    • 1
  1. 1.Physics DepartmentUniversidade do MinhoBragaPortugal

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