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.

HfO _2的新型紧凑模型基于铁电隧道结（FTJ）的忆阻器是基于详细的物理模型，使用经过校准的TCAD仿真来构造的。证明了铁电材料的多畴配置可产生FTJ的准连续电导。事实证明，此行为可以实现强大的依赖于尖峰时序的可塑性类型学习能力，从而使FTJ适合用作尖峰神经网络中的突触忆阻器。使用TCAD–SPICE混合模式和纯SPICE紧凑模型方法，我们将新开发的模型应用于手写数字识别神经形态系统中的交叉开关阵列配置，并证明了80％的成功识别率。应用的方法论证明了在神经形态系统研究中使用TCAD来帮助开发和校准SPICE模型。