A Faithful and Compact Diffusive Memristor Model

Diffusive memristors, characterized by their nonlinear dynamic conductance changes in response to stimulations, enable effective and efficient hardware implementation of various neuromorphic principles and computing algorithms. However, the existing diffusive memristor models either involve overly complicated evolutionary equations that are time-consuming to execute during simulation or miss some important features of the diffusive memristor dynamics that are crucial for many applications. This hindered the progress of algorithm and circuit co-design based on diffusive memristors. Here, we construct a faithful and compact diffusive memristor model capturing the dynamics of the filament length, residue length, channel cross section and stochasticity within the physical devices. Using this model, we successfully replicated the experimentally observed quasistatic behaviors and dynamic behaviors of diffusive memristors. Moreover, our model accurately emulates the high-order dynamic behaviors of leaky integrate-and-fire (LIF) neurons reported in previous literature. These results underscore the significance of our model in enabling precise and efficient algorithm and circuit co-design based on diffusive memristors, paving the way towards the large-scale intelligent systems involving diffusive memristors.