Open AccessIn-materio computing in random networks of carbon nanotubes complexed with chemically dynamic molecules: a review
Author(s) -
Hirofumi Tanaka,
Saman Azhari,
Yuki Usami,
Deep Banerjee,
Takumi Kotooka,
O. Srikimkaew,
Thien Tan Dang,
S. Murazoe,
Rikuto Oyabu,
Kouki Kimizuka,
Masaya Hakoshima
Publication year - 2022
Publication title -
neuromorphic computing and engineering
Language(s) - English
Resource type - Journals
ISSN - 2634-4386
DOI - 10.1088/2634-4386/ac676a
Subject(s) - neuromorphic engineering , carbon nanotube , computer science , nanotechnology , nonlinear system , materials science , distributed computing , data science , artificial neural network , artificial intelligence , physics , quantum mechanics
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.