Practical issues for configuring carbon nanotube composite materials for computation

Abstract

We report our experiences of attempting to configure a single-walled carbon nanotube (SWCNT) / polymer composite material deposited on a micro-electrode array to carry out two classification tasks based on data sets from University of California, Irvine (UCI)[1]. The tasks are attempted using hybrid “in materio” computation: a technique that uses machine search to configure materials for computation. The SWCNT / polymer composite materials are configured using static voltages so that voltage output readings from the material predict which class the data samples belong to. Our initial results suggest that the configured SWCNT materials are able to achieve good levels of predictive accuracy. However, we are in no doubt that the time and effort required to configure the samples could be improved. The parameter space when dealing with physical systems is large, often unknown and slow to test, making progress in this field difficult. Our purpose is not demonstrate the accuracy of configured samples to perform a certain classification, but to showcase the potential of configuring very small material samples with analogue voltages to solve stand alone computation tasks. Such SWCNT devices would be cheap to manufacture and require only low precision assembly, yet if correctly configured would be able to function as multipurpose, single task computational devices.