In-materio computing in random networks of carbon nanotubes complexed with chemically dynamic molecules: a review. Issue 2 (1st June 2022)

• Record Type: Journal Article
• Authors: Tanaka, H; Azhari, S; Usami, Y; Banerjee, D; Kotooka, T; Srikimkaew, O; Dang, T-T; Murazoe, S; Oyabu, R; Kimizuka, K; Hakoshima, M
• Is Part Of: Neuromorphic computing and engineering. Volume 2:Issue 2(2022)
• Publication Date: 2022-06-01
• Subjects: reservoir computing -- neuromorphic -- neural network -- deep learning -- non-linearity -- artificial intelligence; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/ac676a
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: The need for highly energy-efficient information processing has sparked a new age of material-based computational devices. Among these, random networks (RNWs) of carbon nanotubes (CNTs) complexed with other materials have been extensively investigated owing to their extraordinary characteristics. However, the heterogeneity of CNT research has made it quite challenging to comprehend the necessary features of in-materio computing in a RNW 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.