A SPICE compact model for forming-free, low-power graphene-insulator-graphene ReRAM technology
Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered TiOx/Al2O3/TiO2 dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the TiOx and TiO2 layers and by the modulation of a tunnelling gap within the Al2O3 layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. This work shows promise for simulation of large-scale networks of graphene-based low-power ReRAM technology.
