In a paper published in the Journal of Applied Physics, the group describes using single walled carbon nanotube composites (SWCNTs) as a material in 'unconventional' computing. By studying the mechanical and electrical properties of the materials, the team discovered a correlation between the concentration, viscosity and conductivity of SWCNTs and the computational capability of the composite.
"Instead of creating circuits from arrays of discrete components, our work takes a random disordered material and then 'trains' the material to produce a desired output," said Mark Massey from Durham University.
This approach – called 'evolution in materio' – blends materials science, engineering and computer science. Although still in its early stages, the concept has already shown that, by using an approach similar to evolution, materials can be trained to mimic electronic circuits without thneeding to design the material structure in a specific way.
"The material we use in our work is a mixture of carbon nanotubes and polymer, which creates a complex electrical structure," explained Massey. "When voltages (stimuli) are applied at points of the material, its electrical properties change. When the correct signals are applied to the material, it can be trained – or 'evolved' – to perform a useful function."
While the group doesn't expect to see its method compete with high silicon computers, it believes it could turn out to be a complementary technology. "With more research, it could lead to new techniques for making electronics devices," he noted, adding future applications could include analogue signal processing or low power, low cost devices.
