Coupled Ionic–Electronic Charge Transport in Organic Neuromorphic Devices. Issue 6 (23rd February 2022) - Felder, Daniel; Femmer, Robert; Bell, Daniel; Rall, Deniz; Pietzonka, Dirk; Henzler, Sebastian; Linkhorst, John; Wessling, Matthias
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. Abstract : A simulation is presented to describe charging, discharging, and nonideal behavior in organic neuromorphic devices. A Nernst–Planck–Poisson approach combined with a self‐discharge mechanism based on the general Frumkin–Butler–Volmer equation reproduces validation experiments and quantifies the complex system response of conductive polymers in contact with electrolytes under nonideal conditions. This enables the design of resilient neural networks on organic neuromorphic hardware.