Room temperature demonstration of in-materio reservoir computing for optimizing Boolean function with single-walled carbon nanotube/porphyrin-polyoxometalate composite

Deep Banerjee, Saman Azhari, Yuki Usami, Hirofumi Tanaka*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A method for room temperature demonstration of in-materio reservoir computing (RC) with a single-walled carbon nanotube/porphyrin-polyoxometalate network (SWNT/Por-POM) is proposed. Boolean functions of OR, AND, NOR, NAND, XOR, and XNOR, all were reconstructed with an accuracy >90% via supervised training of linear voltage readouts. The RC pre-requisite of echo-state property and recurrent connection allowed for consistent performances over multiple test datasets and time-shifted target sequences. Moreover, a non-zero machine intelligence index confirmed the presence of negative differential resistance dynamics, incorporating in SWNT/Por-POM the mathematical equivalence of additive and subtractive functions, thereby aiding the construction of such complex Boolean functions.

Original languageEnglish
Article number105003
JournalApplied Physics Express
Volume14
Issue number10
DOIs
Publication statusPublished - 2021 Oct
Externally publishedYes

ASJC Scopus subject areas

  • Engineering(all)
  • Physics and Astronomy(all)

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