A polymer wire wetware synapse with improved endurance for physical neuromorphic implementation

Abstract

Neuromorphic computing requires synaptic devices that can reliably update and retain weights under repeated electrical stimulation. Among the material platforms being explored for this purpose, organic wetware synaptic systems are of interest because their switching behavior is directly influenced by the surrounding electrochemical environment. Herein, we report a two-terminal (hydroxymethyl)-3,4-ethylenedioxythiophene:sodium dodecyl benzene sulfonate (PEDOT–MeOH:SDBS) polymer-wire synaptic device operated in ethylene glycol (EG) and water. The device exhibits long-term synaptic plasticity induced by repeated voltage pulses. Under EG operation, the device endures ≥1000 bidirectional conductance switching cycles with stable cyclic voltammetry features and reduced charge-transfer resistance after cycling. In contrast, aqueous operation leads to switching endurance degradation, peak shifts in I–V curve, and increased charge-transfer resistance. Scanning electron microscopy and Raman spectroscopy showed that EG operation is associated with a granular surface morphology and comparatively preserved backbone structure, whereas aqueous operation leads to smoother, more deteriorated surface features, and increased structural disorder. To demonstrate device applicability, conductance states of the polymer wires were mapped to kernels in a convolutional neural network (CNN) for digit recognition, achieving 96% accuracy after 450 epochs. The findings show that operation in EG improves the switching endurance of PEDOT–MeOH:SDBS polymer-wire synapse and that their conductance states can be implemented as physical weights for neural networks. This work highlights electrochemical media engineering as a key design strategy for scalable neuromorphic platforms.