Computation performance of in-materio reservoir device was evaluated by varying intensity of noise injection. Materials for the reservoir device was synthesized using a α-Fe2O3/Ti–Bi–O composite by using the sol–gel method. The prepared samples were characterized by conducting X-ray diffractmetry, transmission electron microscopy, and energy dispersive X-ray spectroscopy to confirm a presence of α-Fe2O3, TiO2, and Bi4Ti3O12 nanoparticles. The I–V and V–t curves show nonlinearity, and phase differences between input and output signals, and the fast Fourier transform of the V–t curve showed high harmonics at the input sine wave with 11Hz of frequency. In the waveform prediction task, the prediction accuracy was improved only when a small intensity of white noise voltage was superimposed to the input information signal.

The need for highly energy-efficient information processing has sparked a new age of material-based computational devices. Among these, random networks of carbon nanotubes (CNTs) complexed with other materials have been extensively investigated owing to their extraordinary characteristics. However, the heterogeneity of carbon nanotube (CNT) research has made it quite challenging to comprehend the necessary features of in-materio computing in a random network of CNTs. Herein, we systematically tackle the topic by reviewing the progress of CNT applications, from the discovery of individual CNT conduction to their recent uses in neuromorphic and unconventional (reservoir) computing. This review catalogues the extraordinary abilities of random CNT networks and their complexes used to conduct nonlinear in-materio computing tasks as well as classification tasks that may replace current energy-inefficient systems.