TCAD modeling of neuromorphic systems based on ferroelectric tunnel junctions

A new compact model for HfO2-based ferroelectric tunnel junction (FTJ) memristors is constructed based on detailed physical modeling using calibrated TCAD simulations. A multi-domain configuration of the ferroelectric material is demonstrated to produce quasi-continuous conductance of the FTJ. This behavior is demonstrated to enable a robust spike-timing-dependent plasticity-type learning capability, making FTJs suitable for use as synaptic memristors in a spiking neural network. Using both TCAD-SPICE mixed-mode and pure SPICE compact model approaches, we apply the newly developed model to a crossbar array configuration in a handwritten digit recognition neuromorphic system and demonstrate an 80% successful recognition rate. The applied methodology demonstrates the use of TCAD to help develop and calibrate SPICE models in the study of neuromorphic systems.