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Microsystem Technologies

, Volume 15, Issue 3, pp 407–412 | Cite as

Surface activation using remote plasma for silicon to quartz wafer bonding

  • R. E. Belford
  • S. Sood
Technical Paper

Abstract

A pre-wafer-bonding remote-plasma treatment is presented as a route to hydrophilic elevated temperature wafer bonding. This particular remote plasma technique was used as a non-etching surface activation technique. We report a post anneal surface energy of 2,250 mJ/m2 using remote plasma activation which is higher than other reported results. Furthermore, the trend in increasing surface energy with increased with plasma exposure time showed no tail-off. The surface damage present in other plasma techniques is avoided and as a consequence exposure times could be extended. An increase in pre-bonding remote plasma treatment times resulted in increased surface energy, this held true for; initial pre-anneal bonding where van der Waals forces hold the wafers together, for ambient covalent bonding, and, for elevated temperature covalent bonding. It also held true for homo and hetero-wafer bonding. Increased surface energy was evident over a variety of bonding and annealing conditions. Surface energies or more descriptively, bonding energies result are reported for Si to Si and Si to quartz bonding and compared with various other pre-bonding treatments. Attention is focused on results obtained by varying different plasma parameters; exposure time, gas species, etc., and their effect on the surface energy of bonded pair. For silicon to quartz, we report an annealing temperature of 200°C without de-bonding, which is an extension of 50°C over previously reported values. Pre- and post plasma surface roughness measurements were made using an atomic force microscope while bond interface was characterized using scanning acoustic microscopy.

Keywords

Surface Energy Wafer Bonding SIMOX Remote Plasma Increase Surface Energy 
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

This research has been supported by National Science Foundation under SBIR Phase II Award No. DMI—0421948.

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Belford Research Inc., Braidwood House SilverburnMidlothianScotland, UK
  2. 2.Faculty of Science and EngineeringThe University of EdinburghEdinburghScotland, UK
  3. 3.SUSS Microtec, Waterbury CenterWaterburyUSA

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