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

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.