Journal of Materials Science

, Volume 48, Issue 20, pp 7204–7214 | Cite as

Conventional and microwave-assisted processing of Cu-loaded ICAs for electronic interconnect applications

  • Siyuan Qi
  • Bala VaidhyanathanEmail author
  • David Hutt


Isotropically conductive adhesives (ICAs) and inks are widely used for interconnecting components and for printed circuits. Silver (Ag)-filled ICAs and inks are the most popular due to their high conductivity and good reliability. However, the price of Ag is a significant issue for the wider exploitation of these materials in low cost, high volume applications such as printed electronics. In addition, there is a need to develop systems compatible with temperature-sensitive substrates through the use of alternative materials and heating methods. Copper (Cu) is considered as a more cost-effective filler for ICAs and in this work, Cu powders were treated to remove the oxide layer and then protected with a self-assembled monolayer. The treated Cu powder was combined with one of two different adhesive resins to form ICAs that were stencil printed onto glass substrates before curing. The use of conventional and microwave-assisted heating methods under an inert atmosphere for the curing of the Cu-loaded ICAs was investigated in detail. The samples were characterised for electrical performance, microstructure and shrinkage as a function of curing temperature (80–150 °C) and time. Tracks with electrical conductivity comparable to Ag-filled adhesives were obtained for both curing methods and with both resins. It was found that curing could be accelerated and/or carried out at lower temperature with the addition of microwave radiation for one adhesive resin, but the other showed almost no absorption indicating a difference in mechanism for the two formulations.


Microwave Microwave Heating Microwave Absorption Thermo Gravimetric Analysis Conductive Adhesive 
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.



The authors thank the Materials Research School (MRS) at Loughborough University for funding this research. Special thanks to Prof. Rachel Thomson and Prof. Jon Binner for their support.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of MaterialsLoughborough UniversityLoughboroughUK
  2. 2.Wolfson School of Mechanical and Manufacturing EngineeringLoughborough UniversityLoughboroughUK

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