Logic Gate and Circuit Training on Randomly Dispersed Carbon Nanotubes

This paper presents results of computations based on threshold logic performed by a thin solid film, following the general principle of evolution in materio. The electrical conductivity is used as the physical property manipulated for evolving Boolean functions. The material used consists of a composite of single-wall carbon nanotubes (SWCNTs) and the polymer poly(methyl methacrylate). The SWCNTs are randomly dispersed in the polymer forming a complex conductive network at the nano-scale. The training is formulated as an optimisation problem with continuous and binary constraints and is subsequently solved by two derivative-free algorithms, the Nelder-Mead (NM) and the Differential Evolution (DE) algorithms. This approach has been used to evolve gates and circuits. The NM fails to converge for all computational tasks, whereas the DE is always successful. The computation tasks considered are simple threshold logic gates and more complicated circuits. The thin film composite is very stable and its behavior remains the same after the optimal solution has been achieved.