Time-Dependent Current Transport Model for Ferroelectric Tunnel Junctions

Memristors are nonlinear resistors with memory, capable of multiple nonvolatile resistance states. They promise to break through the von Neumann bottleneck, enhance computing speed, and reduce device scaling, ultimately enabling advanced artificial intelligence (AI) computing. Ferroelectric memristors, which modulate resistance through electric-field-induced polarization switching, are considered leading candidates for neuromorphic computing and hold great promise for advancing AI. Understanding their mechanism is key to improving real-world performance. A time-dependent current transport model for ferroelectric memristors with ultrathin ferroelectric layers, i.e., ferroelectric tunnel junctions (FTJs), integrating the Thomas-Fermi screening theory, nonequilibrium Green's Function (NEGF), and polarization reversal dynamics, has been developed to estimate their current response. An optimized processing is proposed to save computational effort. It is assumed that the up and down polarization states of the ferroelectric film are likely to occur at a given voltage. These two states are treated equally to calculate the corresponding potential profiles. Based on these potential results, standard currents of FTJs in these two states are computed by using the time-consuming NEGF method. A particular multidomain polarization switching model is proposed to estimate proportions of two polarization states in the FTJ ferroelectric film at a specific voltage. Based on this model, not only the coercive field but also the polarization reversal speed of a thin film can be estimated. Then, the current response to input voltage is computed as a linear combination of each standard current weighted by its corresponding proportion. An ionic two-dimensional van der Waals (2D vdW) material, CuInP2S6 (CIPS), regarded as an ideal ferroelectric material, is taken to construct model FTJs to test our proposed time-dependent current transport model. Finally, the current response of CIPS-based FTJs to continuously varying input voltage is estimated to well measure their synaptic functions for neuromorphic computing. Our developed model provides an effective approach to not only quickly compute current-voltage curves of FTJs but also accurately simulate their synaptic functions without experiments, accelerating the research and development of these devices.