Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic Devices

Abstract Artificial Neural Networks (ANN) require a better platform to reduce their energy consumption and achieve their full potential. Electrochemical devices like the Electrochemical Neuromorphic Organic Device (ENODe) stand out as a potential building block for ANNs, due to their lower energy demand, in addition to their biocompatibility and access to multiple and stable memory levels. However, the non‐volatile effect observed in these devices is not yet fully understood. Hence, here we propose a 2D drift‐diffusion model that is capable to reproduce the device behavior. The model relies on the assumption of trapping sites for cations, which are increasingly filled or emptied during subsequent pre‐synaptic pulses. The model is verified by experiments on devices with varying post‐synaptic dimensions. Overall, the results provide a framework to discuss ENODe operation and design strategies for ENODes with well‐controlled memory states.