A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate

TLDR

Neuromorphic hardware, inspired by neuroscience, requires dense spiking networks, yet current devices have far lower integration density than the human brain. This study introduces a molecular neuromorphic device comprising a highly dense network of single‑walled carbon nanotubes complexed with polyoxometalate. The device operates via electron‑cascading across heterogeneous SWNT/POM junctions, and demonstrates rudimentary learning through reservoir computing. Experimentally, the SWNT/POM network produces spontaneous spikes and noise, with electron‑cascading models matching observations and suggesting that complex functional networks can be built from such molecular devices.

Abstract

Abstract In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities. However, the integration density of present neuromorphic devices is much less than that of human brains. In this report, we present molecular neuromorphic devices, composed of a dynamic and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). We show experimentally that the SWNT/POM network generates spontaneous spikes and noise. We propose electron-cascading models of the network consisting of heterogeneous molecular junctions that yields results in good agreement with the experimental results. Rudimentary learning ability of the network is illustrated by introducing reservoir computing, which utilises spiking dynamics and a certain degree of network complexity. These results indicate the possibility that complex functional networks can be constructed using molecular devices, and contribute to the development of neuromorphic devices.