Coupled Ionic–Electronic Charge Transport in Organic Neuromorphic Devices

Abstract

Conductive polymer devices with tunable resistance allow low-energy, linear programming for efficient neuromorphic computing. Depolarizing impurities, however, are difficult to exclude and limit device performance through nonideal writes and self-discharge. It is shown that these phenomena can be numerically described by combining two-phase charge transport models with electrochemical self-discharge. The simulations accurately reproduce the experimental data, including cyclic voltammetry and standard neuromorphic functions, such as linear programming of discrete states and short-term potentiation. Impurities affect device write accuracy significantly for long programming times above 1000 ms. The effect is reduced to 0.03% for shorter times. Self-discharge is impacted by device potential as well as impurity concentration. A model-based trade-off between operating parameters nearly triples the number of usable conductance states at ambient conditions. Understanding these device limitations as well as workarounds is a vital step toward the implementation of neuromorphic device networks.