Fabrication and Training of 3D Conductive Polymer Networks for Neuromorphic Wetware

Abstract

The human brain possesses an exceptional information processing capability owing to the 3D and dense network architecture of numerous neurons and synapses. Brain‐inspired neuromorphic hardware can also benefit from 3D architectures, such as high integration of circuits and acquisition of highly complex dynamical systems. In this study, for future 3D neuromorphic engineering, 3D conductive polymer networks consisting of poly(3,4‐ethylenedioxy‐thiophene) doped with poly(styrene sulfonate) anions (PEDOT:PSS) are successfully and stably fabricated between multiple electrodes from scratch in precursor solution by electropolymerization. The networks efficiently emulate the 3D local connections between neighboring neurons observed in the cortex. This novel technology, which allows 3D conductive wiring only between desired electrodes, is unprecedented and has potential as an underlying technology for 3D integration. Furthermore, the experimental results also conclusively prove that conductance modification can be performed by manipulating the physical and chemical properties of 3D branch‐wired conductive polymer wires, thus demonstrating for the first time the feasibility of neuromorphic wetware with enhanced biological plausibility in the subsequent post‐Moore era.

Inverted input method for computing performance enhancement of the ion-gating reservoir

“…Among various state-of-art neuromorphic computing approaches, [1][2][3][4][5][6] physical reservoir computing (PRC) is a particularly promising computation scheme to reduce energy consumption in AI-based information processing. 1) In this scheme, materials and devices are utilized as physical reservoirs to efficiently process information, by mapping input to a higher-dimensional feature space using their own intrinsic nonlinearity.…”